diff --git a/Submissions/002799697_Yanyan_Chen/002799697_Yanyan_Chen_Assignment4.ipynb b/Submissions/002799697_Yanyan_Chen/002799697_Yanyan_Chen_Assignment4.ipynb new file mode 100644 index 0000000..cdfaff9 --- /dev/null +++ b/Submissions/002799697_Yanyan_Chen/002799697_Yanyan_Chen_Assignment4.ipynb @@ -0,0 +1,2127 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# INFO 6205 – Program Structure and Algorithms Assignment 4 Solutions\n", + "## Name: Yanyan Chen\n", + "## NUID: 002799697\n", + "## Date: 11/19/2023\n", + "\n", + "***Note: 1. some snippets of code are pseudo-code, for better explaining and answering questions, and don't really run. 2. All the graphics are created by me and are all original!**" + ], + "metadata": { + "id": "MEiyuZw68mET" + } + }, + { + "cell_type": "markdown", + "source": [ + "## **Q1:**\n", + "\n", + "Consider a weighted undirected graph $G=(V,E)$ where each edge has a positive weight. A \"minimum spanning tree\" (MST) of a graph is a subset of its edges that connects all vertices together without any cycles and with the minimum possible total edge weight.\n", + "\n", + "A. Is the problem of finding a minimum spanning tree for a given graph in P? If so, provide a proof.\n", + "\n", + "B. Suppose we alter the problem such that we need to find a spanning tree where the maximum edge weight is minimized (known as the \"minimum bottleneck spanning tree\"). Is this problem in P? If so, prove it.\n", + "\n", + "C. Consider a variant where we need to find a spanning tree with exactly $k$ edges and the minimum possible total weight, where $k$ is a given integer. Is this problem in NP? If so, provide a proof.\n", + "\n", + "D. Is the variant in part C NP-complete? If so, provide a proof.\n", + "\n", + "###**Answer:**\n", + "\n", + "#### Question Breakdown\n", + "1. Finding a Minimum Spanning Tree (MST) in P\n", + "2. Minimum Bottleneck Spanning Tree in P\n", + "3. Spanning Tree with Exactly $k$ Edges in NP\n", + "4. NP-Completeness of the $k$-Edge Spanning Tree Problem\n", + "\n", + "#### **Part A: Is Finding an MST in P?**\n", + "\n", + "Solution Steps:\n", + "\n", + "1. **Understand the Problem:** We need to find a spanning tree of a graph such that the sum of the weights of its edges is minimized.\n", + "2. **Algorithm:** Use Kruskal's or Prim's algorithm, both of which are known to efficiently find the MST.\n", + "3. **Pseudo-code for Kruskal's Algorithm:**\n", + "\n", + "*** *Attention: it's pseudocode for better explanation. It's not runnable.***" + ], + "metadata": { + "id": "OXilDH8TGxBJ" + } + }, + { + "cell_type": "code", + "source": [ + "def kruskal(graph):\n", + " mst = set()\n", + " # Sort edges in increasing order based on weight\n", + " edges = sorted(graph.edges(), key=lambda e: e.weight)\n", + " # Initialize disjoint sets for each vertex\n", + " for vertex in graph.vertices():\n", + " make_set(vertex)\n", + " # Iterate over sorted edges\n", + " for edge in edges:\n", + " u, v = edge.vertices()\n", + " if find_set(u) != find_set(v):\n", + " mst.add(edge)\n", + " union(u, v)\n", + " return mst" + ], + "metadata": { + "id": "AonpKC4P0ZTw" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "4. Proof: Both Kruskal's and Prim's algorithms run in polynomial time, thus proving that finding an MST is in P." + ], + "metadata": { + "id": "AAiKY8VG1RGW" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### **Part B: Is Minimum Bottleneck Spanning Tree in P?**\n", + "\n", + "Solution Steps:\n", + "\n", + "1. **Understand the Problem:** Find a spanning tree where the heaviest edge is as light as possible.\n", + "\n", + "2. **Algorithm:** Modify Kruskal's algorithm to stop when $n−1$ edges are included, where $n$ is the number of vertices.\n", + "\n", + "3. **Pseudo-code Modification:**\n", + "* Add a counter for the number of edges added to the MST. Stop adding edges once the count reaches $n−1$.\n", + "4. **Proof:** This modified algorithm also runs in polynomial time, proving the problem is in P." + ], + "metadata": { + "id": "__Ip_-591UqZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### **Part C: Is the $k$-Edge Spanning Tree Problem in NP?**\n", + "\n", + "Solution Steps:\n", + "\n", + "1. **Understand the Problem:** Find a spanning tree with exactly $k$ edges that has the minimum total weight.\n", + "2. **Non-determinism:** A non-deterministic algorithm guesses a combination of $k$ edges and checks if it forms a spanning tree with the minimum total weight.\n", + "3. **Pseudo-code:**\n", + "*** *Attention: it's pseudocode for better explanation. It's not runnable.***" + ], + "metadata": { + "id": "EiJu-KZO1_jI" + } + }, + { + "cell_type": "code", + "source": [ + "def k_edge_spanning_tree(graph, k):\n", + " # Non-deterministically guess a combination of k edges\n", + " guessed_edges = non_deterministic_guess(graph.edges(), k)\n", + " # Check if guessed edges form a spanning tree\n", + " if is_spanning_tree(graph, guessed_edges) and total_weight(guessed_edges) is minimal:\n", + " return guessed_edges\n", + " return None\n" + ], + "metadata": { + "id": "_3z593_M2RXH" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "4. **Proof:** Since we can verify a solution in polynomial time, the problem is in NP." + ], + "metadata": { + "id": "1H9dNpr42YYd" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### **Part D: Is the $k$-Edge Spanning Tree Problem NP-Complete?**\n", + "\n", + "Solution Steps:\n", + "\n", + "1. **Understand the Problem:** Proving NP-completeness involves showing the problem is in NP and that any problem in NP can be reduced to it in polynomial time.\n", + "\n", + "2. **Reduction Approach:** Show how a known NP-complete problem, like the Hamiltonian Path problem, can be reduced to this problem.\n", + "\n", + "3. **Reduction Pseudo-code:**\n", + "* Describe a polynomial-time algorithm that transforms instances of the Hamiltonian Path problem into instances of the $k$-edge spanning tree problem.\n", + "4. **Proof:** The reduction, coupled with the fact that the problem is in NP (as shown in part C), proves NP-completeness.\n" + ], + "metadata": { + "id": "miERMWUw3cPz" + } + }, + { + "cell_type": "markdown", + "source": [ + "To provide a detailed step-by-step solution for each part of the newly designed question on graph theory, we'll explore the concepts and algorithms related to minimum spanning trees, minimum bottleneck spanning trees, and variations involving specific constraints on the number of edges." + ], + "metadata": { + "id": "pC4jpIcp48IS" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### **Part A: Is Finding an MST in P?**\n", + "**Objective:** Determine if finding a Minimum Spanning Tree (MST) for a given undirected, weighted graph is in the complexity class P (Polynomial time).\n", + "\n", + "**Solution Steps:**\n", + "\n", + "1. **Definition:** An MST of a graph is a subset of its edges that connects all vertices together without any cycles and with the minimum possible total edge weight.\n", + "\n", + "2. **Algorithmic Approach:** Kruskal’s or Prim’s algorithm can be used to find an MST. Both algorithms are known to run in polynomial time.\n", + "\n", + "3. **Proof:**\n", + "\n", + "* Kruskal’s Algorithm sorts the edges by weight and then iteratively adds the smallest edge to the MST, provided it doesn’t create a cycle. This can be done efficiently using a disjoint-set data structure. The sorting step is $O(ElogE)$ and the union-find operation takes $O(logV)$ time, making the overall complexity $O(ElogE)$, which is polynomial.\n", + "* Prim’s Algorithm grows the MST by adding the smallest edge that connects a vertex in the MST to a vertex outside the MST. Using a priority queue, this can be done in $O(E+VlogV)$ time, which is also polynomial.\n", + "\n", + "4. **Conclusion:** Since both Kruskal's and Prim's algorithms find an MST in polynomial time, the problem of finding an MST is in P.\n", + "\n", + "\n", + "#### **Part B: Is Minimum Bottleneck Spanning Tree in P?**\n", + "**Objective:** Determine if finding a Minimum Bottleneck Spanning Tree (MBST) is in P.\n", + "\n", + "**Solution Steps:**\n", + "\n", + "1. **Definition:** An MBST is a spanning tree where the maximum edge weight is minimized.\n", + "\n", + "2. **Algorithmic Approach:** A modified version of Kruskal’s or Prim’s algorithm can be used. The goal is to minimize the heaviest edge in the tree.\n", + "\n", + "3. **Proof:**\n", + "\n", + "The modification involves selecting edges similar to the MST algorithms but prioritizing the minimization of the maximum edge weight. The complexity remains polynomial as the core steps of sorting and selecting edges do not change significantly in terms of computational complexity.\n", + "\n", + "4. **Conclusion:** Finding an MBST can be achieved in polynomial time, and thus, the problem is in P.\n", + "\n", + "####**Part C: Is the $k$-Edge Spanning Tree Problem in NP?**\n", + "\n", + "**Objective:** Determine if finding a spanning tree with exactly $k$ edges that has the minimum total weight is in NP.\n", + "\n", + "**Solution Steps:**\n", + "\n", + "1. **Definition:** This problem involves finding a spanning tree with a fixed number of edges, $k$, that minimizes the total weight.\n", + "\n", + "2. **Characteristics of NP:** A problem is in NP if a solution can be verified in polynomial time.\n", + "\n", + "3. **Verification Approach:** Given a spanning tree, we can verify in polynomial time if it has exactly $k$ edges and calculate its total weight to confirm if it's minimal.\n", + "\n", + "4. Conclusion: The $k$-Edge Spanning Tree Problem is in NP because any given solution (a spanning tree with $k$ edges) can be verified for correctness in polynomial time.\n", + "\n", + "#### **Part D: Is the $k$-Edge Spanning Tree Problem NP-Complete?**\n", + "\n", + "**Objective:** Determine if the $k$-Edge Spanning Tree Problem is NP-Complete.\n", + "\n", + "**Solution Steps:**\n", + "\n", + "1. **Definition of NP-Complete:** A problem is NP-Complete if it is in NP and every problem in NP can be reduced to it in polynomial time.\n", + "\n", + "2. **Already in NP:** From Part C, we know this problem is in NP.\n", + "\n", + "3. **Reduction Approach:** To prove NP-Completeness, we need to show that a known NP-Complete problem can be reduced to this problem in polynomial time. A candidate problem for reduction could be the Hamiltonian Path problem, where the goal is to find a path that visits each vertex exactly once.\n", + "\n", + "4. **Reduction Proof:** We would construct a reduction that translates an instance of the Hamiltonian Path problem to an instance of the $k$-Edge Spanning Tree Problem. The details of this reduction would involve mapping the vertices and edges in a way that solving the $k$-Edge Spanning Tree Problem would effectively solve the Hamiltonian Path problem.\n", + "\n", + "5. **Conclusion:** If such a polynomial-time reduction can be constructed, it would prove that the $k$-Edge Spanning Tree Problem is NP-Complete. However, this proof would require a detailed construction of the reduction, which is beyond the scope of this explanation." + ], + "metadata": { + "id": "hTVR65zB5CRR" + } + }, + { + "cell_type": "markdown", + "source": [ + "Here is the runnable python code for better explain and solve this question\n", + "#### Part A: Python Code for Finding a Minimum Spanning Tree (MST)\n", + "We can use Kruskal's algorithm to find an MST. Here is a simplified version of the algorithm:" + ], + "metadata": { + "id": "wdt4Yowl837J" + } + }, + { + "cell_type": "code", + "source": [ + "class DisjointSet:\n", + " def __init__(self, vertices):\n", + " self.parent = {v: v for v in vertices}\n", + " self.rank = {v: 0 for v in vertices}\n", + "\n", + " def find(self, item):\n", + " if self.parent[item] != item:\n", + " self.parent[item] = self.find(self.parent[item])\n", + " return self.parent[item]\n", + "\n", + " def union(self, set1, set2):\n", + " root1 = self.find(set1)\n", + " root2 = self.find(set2)\n", + " if root1 != root2:\n", + " if self.rank[root1] > self.rank[root2]:\n", + " self.parent[root2] = root1\n", + " else:\n", + " self.parent[root1] = root2\n", + " if self.rank[root1] == self.rank[root2]:\n", + " self.rank[root2] += 1\n", + "\n", + "def kruskal(graph):\n", + " vertices, edges = graph\n", + " disjoint_set = DisjointSet(vertices)\n", + " mst = []\n", + " edges.sort(key=lambda e: e[2]) # Sorting edges by weight\n", + "\n", + " for edge in edges:\n", + " u, v, weight = edge\n", + " if disjoint_set.find(u) != disjoint_set.find(v):\n", + " disjoint_set.union(u, v)\n", + " mst.append(edge)\n", + "\n", + " return mst\n", + "\n", + "# Example usage\n", + "vertices = ['A', 'B', 'C', 'D', 'E']\n", + "edges = [('A', 'B', 1), ('B', 'C', 3), ('A', 'D', 4), ('D', 'E', 2), ('B', 'E', 2), ('C', 'E', 3)]\n", + "graph = (vertices, edges)\n", + "\n", + "mst = kruskal(graph)\n", + "print(\"Minimum Spanning Tree:\", mst)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K9zovEry3ArZ", + "outputId": "5894e887-6dab-4689-81c4-339d46932b9c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Minimum Spanning Tree: [('A', 'B', 1), ('D', 'E', 2), ('B', 'E', 2), ('B', 'C', 3)]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Part B: Python Code for Minimum Bottleneck Spanning Tree\n", + "Since the MBST often aligns with the MST, we'll reuse the MST code but with a focus on minimizing the maximum edge weight." + ], + "metadata": { + "id": "33azrlFU8_XV" + } + }, + { + "cell_type": "code", + "source": [ + "# Reusing the DisjointSet class and Kruskal's algorithm from Part A\n", + "# Assuming the graph is the same as in Part A\n", + "\n", + "def minimum_bottleneck_spanning_tree(graph):\n", + " # The MST is typically also the MBST\n", + " return kruskal(graph)\n", + "\n", + "# Example usage\n", + "mbst = minimum_bottleneck_spanning_tree(graph)\n", + "print(\"Minimum Bottleneck Spanning Tree:\", mbst)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wpCXU_aD4PmT", + "outputId": "12896880-d324-4cab-802e-df0ea199c549" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Minimum Bottleneck Spanning Tree: [('A', 'B', 1), ('D', 'E', 2), ('B', 'E', 2), ('B', 'C', 3)]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Part C: Python Code for a Spanning Tree with Exactly $k$ Edges\n", + "For this part, we can implement a brute-force approach that tries all combinations of $k$ edges and checks if they form a spanning tree. This is not efficient for large graphs." + ], + "metadata": { + "id": "q-ppK5B19Ddw" + } + }, + { + "cell_type": "code", + "source": [ + "from itertools import combinations\n", + "\n", + "def is_spanning_tree(vertices, edges):\n", + " disjoint_set = DisjointSet(vertices)\n", + " for u, v, _ in edges:\n", + " if disjoint_set.find(u) != disjoint_set.find(v):\n", + " disjoint_set.union(u, v)\n", + " else:\n", + " return False\n", + " return len(set(disjoint_set.find(v) for v in vertices)) == 1\n", + "\n", + "def k_edge_spanning_tree(graph, k):\n", + " vertices, edges = graph\n", + " for edge_subset in combinations(edges, k):\n", + " if is_spanning_tree(vertices, list(edge_subset)):\n", + " return list(edge_subset)\n", + " return None\n", + "\n", + "# Example usage\n", + "k = 4 # Adjust k as needed\n", + "k_tree = k_edge_spanning_tree(graph, k)\n", + "print(f\"Spanning Tree with Exactly {k} Edges:\", k_tree)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "odOywZus4SPc", + "outputId": "59791aa2-5bac-46b2-9c60-dbe4da81d830" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Spanning Tree with Exactly 4 Edges: [('A', 'B', 1), ('D', 'E', 2), ('B', 'E', 2), ('B', 'C', 3)]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## **Q2**:\n", + "\n", + "**The Directed Hamiltonian Cycle with Constraints Problem**\n", + "\n", + "**Problem Definition:**\n", + "\n", + "You are given a directed graph $G$ and a set of $m$ constraints in the form of node pairs $(u_1,v_1), (u_2, v_2), ... ,(u_m, v_m)$. The problem is to determine whether there exists a Hamiltonian cycle in $G$ such that for each constraint pair $(u_i, v_i)$, the node $v_i$ is visited immediately after $u_i$ in the cycle.\n", + "\n", + "**Objective:**\n", + "\n", + "Demonstrate that the Directed Hamiltonian Cycle with Constraints Problem is NP-complete.\n", + "\n", + "### **Answer:**\n", + "\n", + "#### **Step 1: Preliminary Understanding**\n", + "\n", + "**Hamiltonian Cycle in Directed Graphs:**\n", + "\n", + "* A Hamiltonian cycle in a directed graph is a cycle that visits each node exactly once.\n", + "* This concept is important because our problem revolves around finding such a cycle under certain constraints.\n", + "\n", + "#### **Step 2: Reduction from an NP-complete Problem**\n", + "\n", + "Choosing a Known NP-complete Problem:\n", + "\n", + "* The classic Directed Hamiltonian Cycle (DHC) problem is known to be NP-complete.\n", + "* In the DHC problem, the goal is to determine whether a given directed graph contains a Hamiltonian cycle.\n", + "\n", + "Why Choose DHC for Reduction:\n", + "\n", + "* The DHC problem is closely related to our problem but without the additional constraints.\n", + "* Showing that DHC can be transformed into our problem helps establish the NP-completeness of our problem.\n", + "\n", + "#### **Step 3: Step-by-Step Reduction Process**\n", + "\n", + "Transformation Process:\n", + "\n", + "1. **Input:** An instance of the DHC problem (a directed graph $G$).\n", + "2. **Output:** An instance of the Directed Hamiltonian Cycle with Constraints Problem.\n", + "3. **Process:**\n", + "* Keep the graph $G$ unchanged.\n", + "* Introduce a set of dummy constraints that do not alter the nature of the Hamiltonian cycle in $G$. For instance, for a subset of nodes in $G$, add constraints that are already implicitly satisfied by any Hamiltonian cycle in\n", + "$G$.\n", + "\n", + "**Pseudo-code Illustration:**\n", + "\n", + "*** *Attention: it's pseudocode for better explanation. It's not runnable.***" + ], + "metadata": { + "id": "aZaOZoXwoYgu" + } + }, + { + "cell_type": "code", + "source": [ + "def transform_to_constrained_hamiltonian_cycle(G):\n", + " constraints = []\n", + " for edge in G.edges:\n", + " if some_condition(edge): # Define a condition that doesn't alter Hamiltonian nature\n", + " constraints.append(edge)\n", + " return (G, constraints)" + ], + "metadata": { + "id": "KMlYrunwDHpi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Step 4: Proof of Correctness**\n", + "\n", + "**Correspondence Between Solutions:**\n", + "\n", + "* If $G$ has a Hamiltonian cycle, the same cycle will satisfy the transformed problem with added constraints.\n", + "* Conversely, if the transformed problem has a solution, it implies $G$ has a Hamiltonian cycle, as the constraints do not alter the essential nature of the Hamiltonian cycle.\n", + "\n", + "**Complexity of Transformation:**\n", + "\n", + "* The transformation is polynomial in time, as it involves iterating over the edges of $G$ and adding a polynomial number of constraints.\n", + "\n", + "#### **Step 5: Conclusion**\n", + "\n", + "**NP-completeness of the Problem:**\n", + "\n", + "* Since we can transform an NP-complete problem (DHC) into the Directed Hamiltonian Cycle with Constraints Problem in polynomial time, and the solution to one implies a solution to the other, our problem is NP-hard.\n", + "\n", + "* Additionally, checking whether a given solution is valid (i.e., whether a given cycle is a Hamiltonian cycle that satisfies the constraints) can be done in polynomial time, so our problem is in NP.\n", + "\n", + "**Final Conclusion:**\n", + "\n", + "* By meeting both the criteria of being NP-hard and being in NP, the Directed Hamiltonian Cycle with Constraints Problem is NP-complete." + ], + "metadata": { + "id": "gr8LAEKsDMea" + } + }, + { + "cell_type": "markdown", + "source": [ + "Solving NP-complete problems like the \"Directed Hamiltonian Cycle with Constraints Problem\" in a general case is a complex task, especially since NP-complete problems do not have known polynomial-time algorithms for all instances. However, for small instances or specific types of graphs, it's possible to write code that attempts a solution, typically through exhaustive search or heuristics.\n", + "\n", + "Here's a basic Python framework to approach this problem using brute force. Keep in mind that this approach is highly inefficient for large graphs and is mainly for explaining purposes:" + ], + "metadata": { + "id": "GqmbVJ0KEqIJ" + } + }, + { + "cell_type": "code", + "source": [ + "import itertools\n", + "\n", + "def is_valid_hamiltonian_cycle(graph, cycle, constraints):\n", + " \"\"\"\n", + " Check if the cycle is a valid Hamiltonian cycle given the constraints.\n", + " \"\"\"\n", + " n = len(graph)\n", + " if len(cycle) != n:\n", + " return False\n", + "\n", + " # Check if cycle is Hamiltonian\n", + " for i in range(n):\n", + " if not graph[cycle[i]][cycle[(i + 1) % n]]:\n", + " return False\n", + "\n", + " # Check constraints\n", + " for u, v in constraints:\n", + " if not ((cycle.index(u) + 1) % n == cycle.index(v)):\n", + " return False\n", + "\n", + " return True\n", + "\n", + "def find_hamiltonian_cycle(graph, constraints):\n", + " \"\"\"\n", + " Attempt to find a Hamiltonian cycle in the graph that meets the constraints.\n", + " \"\"\"\n", + " n = len(graph)\n", + " for perm in itertools.permutations(range(n)):\n", + " if is_valid_hamiltonian_cycle(graph, perm, constraints):\n", + " return perm\n", + " return None\n", + "\n", + "# Example graph (adjacency matrix)\n", + "graph = [\n", + " [0, 1, 1, 0],\n", + " [0, 0, 1, 1],\n", + " [1, 0, 0, 1],\n", + " [1, 0, 0, 0]\n", + "]\n", + "\n", + "# Example constraints [(u1, v1), (u2, v2), ...]\n", + "constraints = [(0, 1), (2, 3)]\n", + "\n", + "cycle = find_hamiltonian_cycle(graph, constraints)\n", + "if cycle:\n", + " print(\"Hamiltonian Cycle found:\", cycle)\n", + "else:\n", + " print(\"No Hamiltonian Cycle satisfies the constraints.\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6bJBTcg1EzS3", + "outputId": "f7f51605-b964-4c71-b731-62be1117e3bb" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Hamiltonian Cycle found: (0, 1, 2, 3)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "This code defines a simple directed graph using an adjacency matrix and specifies constraints as pairs of nodes. The find_hamiltonian_cycle function tries all permutations of the nodes to find a Hamiltonian cycle that satisfies the constraints. Remember, this approach is feasible only for small graphs due to its exponential time complexity. For larger graphs, more sophisticated methods like backtracking or advanced heuristics might be required, but they still don't guarantee polynomial-time solutions." + ], + "metadata": { + "id": "Us8P1onVE4mr" + } + }, + { + "cell_type": "markdown", + "source": [ + "## **Q3:**\n", + "\n", + "You are coordinating a multi-disciplinary scientific research project involving n different areas of expertise (e.g., biology, chemistry, physics, mathematics, computer science, etc.) You must assemble a team of researchers where each area of expertise is covered. You have a list of m potential candidates, each with expertise in one or more areas. The challenge is to determine if it is possible to select at most k candidates (where k ≤ m) such that all n areas of expertise are represented on your team. This problem is termed the \"Optimal Research Team\" problem.\n", + "\n", + "Provide a detailed step-by-step solution to this problem. Your solution should include:\n", + "\n", + "1. A formal definition of the problem.\n", + "2. An explanation of why the Optimal Research Team problem is NP-complete.\n", + "3. A pseudo-code outline of an algorithm that attempts to solve this problem.\n", + "4. An analysis of the complexity of your proposed algorithm.\n", + "\n", + "\n", + "###**Answer:**\n", + "\n", + "1. **Problem Definition**\n", + "\n", + "**Input:**\n", + "\n", + "* expertiseAreas: A set of n distinct areas of expertise required for the project (e.g., {\"biology\", \"chemistry\", \"physics\"}).\n", + "* candidates: A list of m potential candidates, where each candidate is associated with a subset of these expertise areas.\n", + "* k: An integer representing the maximum number of candidates to be selected, where k ≤ m.\n", + "\n", + "**Objective:**\n", + "\n", + "* To determine if there is a subset of at most k candidates that collectively covers all n areas of expertise.\n", + "\n", + "This problem is a variant of the set cover problem, where we are trying to cover a set (expertise areas) with as few subsets (candidates' skills) as possible.\n", + "\n", + "\n", + "2. **NP-Completeness Proof**\n", + "\n", + "**Reduction from Set Cover Problem:**\n", + "\n", + "* The Set Cover problem is known to be NP-complete. It involves a universe U and a collection S of subsets of U. The goal is to find the smallest subset of S that covers all elements in U.\n", + "* In the Optimal Research Team problem, each area of expertise is an element of U, and each candidate's skill set is a subset in S.\n", + "* If we can solve the Optimal Research Team problem, we can solve the Set Cover problem, implying that our problem is at least as hard as Set Cover.\n", + "\n", + "**Why NP-Complete:**\n", + "\n", + "* The problem is in NP because, given a set of candidates, we can quickly verify if they cover all expertise areas.\n", + "* The reduction from Set Cover, an NP-complete problem, shows that our problem is NP-hard.\n", + "* Combining these two points, we conclude that the Optimal Research Team problem is NP-complete.\n", + "\n", + "**Understanding NP-Completeness**\n", + "\n", + "* NP (Non-deterministic Polynomial time):\n", + "\n", + " * A problem is in NP if a solution to the problem can be verified in polynomial time. In other words, if given a \"certificate\" or a potential solution, we can check whether it is indeed a valid solution quickly (in polynomial time).\n", + "\n", + "* NP-complete:\n", + "\n", + " * A problem is NP-complete if it satisfies two conditions:\n", + " 1. It is in NP.\n", + " 2. Every problem in NP can be reduced to it in polynomial time.\n", + "\n", + "**The Set Cover Problem**\n", + "\n", + "* Set Cover Problem Description:\n", + "\n", + " * Given a universe U and a collection S of subsets of U, the goal is to find the smallest subset of S whose union equals U.\n", + "\n", + " * This problem is known to be NP-complete.\n", + "\n", + "**Reduction from Set Cover to Optimal Research Team**\n", + "\n", + "* **Step 1: Mapping to the Optimal Research Team Problem**\n", + "\n", + " * Universe U in Set Cover: Corresponds to the set of all expertise areas in the Optimal Research Team problem.\n", + " * Subsets in S in Set Cover: Correspond to the skill sets of the individual candidates in the Optimal Research Team problem.\n", + " * Goal of both problems: In Set Cover, we want the smallest number of subsets that cover all elements in U. In the Optimal Research Team, we are looking for the smallest team that covers all areas of expertise.\n", + "\n", + "* **Step 2: Constructing the Reduction**\n", + "\n", + "* Given an instance of the Set Cover problem (a universe U and a collection S), we can construct an instance of the Optimal Research Team problem by:\n", + " * Treating each element in U as a distinct area of expertise.\n", + " * Treating each subset in S as a candidate's set of expertise.\n", + "\n", + "* **Step 3: Equivalence of Solutions**\n", + "\n", + " * If we can find a solution to the Optimal Research Team problem (a smallest team that covers all areas of expertise), this solution directly translates to a solution to the Set Cover problem (the smallest number of subsets that covers the universe U).\n", + " * Conversely, a solution to the Set Cover problem can be used to solve the Optimal Research Team problem.\n", + "\n", + "**Conclusion**\n", + "\n", + "* Why NP-Complete?\n", + "\n", + " * The Optimal Research Team problem is in NP because, given a team of candidates, we can quickly check (in polynomial time) if all expertise areas are covered.\n", + " * The reduction from Set Cover shows that any problem in NP can be transformed into an instance of the Optimal Research Team problem. This means it is at least as hard as any problem in NP, qualifying it as NP-hard.\n", + " * Since it is both in NP and NP-hard, the Optimal Research Team problem is NP-complete.\n", + "\n", + "\n", + "3. **Pseudo-Code for Proposed Algorithm**\n", + "\n", + "A brute-force approach would be:\n", + "\n", + "*** *Attention: it's pseudocode for better explanation. It's not runnable.***" + ], + "metadata": { + "id": "j2w4dQWE3soy" + } + }, + { + "cell_type": "code", + "source": [ + "def optimalResearchTeam(expertiseAreas, candidates, k):\n", + " # Initialize an empty list to store the selected candidates\n", + " selectedCandidates = []\n", + "\n", + " # Iterate to find a combination of candidates covering all areas\n", + " for each combination of candidates up to size k:\n", + " if combination covers all expertiseAreas:\n", + " selectedCandidates = combination\n", + " break\n", + "\n", + " return selectedCandidates" + ], + "metadata": { + "id": "BJqJT9AWOHaW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "4. **Complexity Analysis**\n", + "\n", + "* Time Complexity:\n", + "\n", + " * The brute-force algorithm's time complexity is O(m^k), as it checks all combinations of candidates up to size k.\n", + "\n", + " * This is exponential and thus very inefficient for large values of m and k.\n", + "\n", + "* Space Complexity:\n", + "\n", + " * The space complexity depends on the storage of combinations and can be significant for large m and k.\n", + "\n", + "**Conclusion**\n", + "\n", + "The Optimal Research Team problem is a computationally challenging problem due to its NP-complete nature. The brute-force solution, while straightforward, is impractical for large datasets, highlighting the need for more efficient, possibly approximate, algorithms for real-world applications.\n", + "\n", + "To better explain this question, let's create a runnable Python code for the \"Optimal Research Team\" problem, we can implement a brute-force approach. This approach will try every possible combination of candidates up to a given size k and check if they cover all the required expertise areas.\n", + "\n", + "However, it's important to note that this brute-force method is not efficient for large input sizes due to the problem's NP-complete nature. The time complexity grows exponentially with the increase in the number of candidates (m) and the maximum team size (k).\n", + "\n", + "Here's a Python script to solve this problem:" + ], + "metadata": { + "id": "_1qc6cMZOKQ0" + } + }, + { + "cell_type": "code", + "source": [ + "from itertools import combinations\n", + "\n", + "def covers_all_expertise_areas(team, expertiseAreas):\n", + " covered = set()\n", + " for member in team:\n", + " covered.update(member['expertise'])\n", + " return covered == expertiseAreas\n", + "\n", + "def optimalResearchTeam(expertiseAreas, candidates, k):\n", + " bestTeam = None\n", + "\n", + " for team_size in range(1, k + 1):\n", + " for team in combinations(candidates, team_size):\n", + " if covers_all_expertise_areas(team, expertiseAreas):\n", + " return team\n", + "\n", + " return bestTeam\n", + "\n", + "# Example usage\n", + "expertiseAreas = {'biology', 'chemistry', 'physics', 'mathematics', 'computer science'}\n", + "candidates = [\n", + " {'name': 'Alice', 'expertise': {'biology', 'chemistry'}},\n", + " {'name': 'Bob', 'expertise': {'physics', 'mathematics'}},\n", + " {'name': 'Charlie', 'expertise': {'computer science'}},\n", + " {'name': 'Dana', 'expertise': {'biology', 'computer science'}}\n", + "]\n", + "k = 3\n", + "\n", + "optimal_team = optimalResearchTeam(expertiseAreas, candidates, k)\n", + "for member in optimal_team:\n", + " print(member['name'])\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mGpZRLNBP_ro", + "outputId": "61fe165d-19ad-4c4c-82dc-4c9b14f24957" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Alice\n", + "Bob\n", + "Charlie\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "This script defines two functions:\n", + "\n", + "1. covers_all_expertise_areas(team, expertiseAreas): Checks if a given team of candidates covers all the required expertise areas.\n", + "\n", + "2. optimalResearchTeam(expertiseAreas, candidates, k): Tries every combination of candidates up to size k to find a team that covers all expertise areas.\n", + "\n", + "In the example, we have a set of expertise areas and a list of candidates with their respective areas of expertise. We are trying to find a team of size up to k that covers all expertise areas.\n", + "\n", + "Remember, for larger values of m and k, this script may take a very long time to run or may not be practical due to its computational complexity." + ], + "metadata": { + "id": "wZ6blVQVQCiS" + } + }, + { + "cell_type": "markdown", + "source": [ + "For the Optimal Research Team problem, which is NP-complete, there isn't an efficient algorithm guaranteed to work for all cases in polynomial time. However, we can use heuristic or approximation algorithms that perform better than brute force on average or in specific scenarios, though they might not always give the optimal solution. One common approach is to use a greedy algorithm.\n", + "\n", + "**Greedy Algorithm Approach**\n", + "\n", + "The greedy algorithm doesn't guarantee an optimal solution but can often find a good enough solution much faster than the brute-force approach. The idea is to repeatedly choose the candidate that covers the largest number of uncovered expertise areas until all are covered or we reach the limit k.\n", + "\n", + "Here's how you could implement this in Python:" + ], + "metadata": { + "id": "gt43H26TBTj7" + } + }, + { + "cell_type": "code", + "source": [ + "def greedyOptimalResearchTeam(expertiseAreas, candidates, k):\n", + " required_expertise = set(expertiseAreas)\n", + " team = []\n", + "\n", + " while required_expertise and len(team) < k:\n", + " best_candidate = None\n", + " best_cover = 0\n", + " for candidate in candidates:\n", + " cover = len(required_expertise.intersection(candidate['expertise']))\n", + " if cover > best_cover:\n", + " best_cover = cover\n", + " best_candidate = candidate\n", + "\n", + " if best_candidate is None:\n", + " break\n", + "\n", + " team.append(best_candidate)\n", + " required_expertise -= best_candidate['expertise']\n", + " candidates.remove(best_candidate)\n", + "\n", + " if required_expertise:\n", + " return None # Not possible to cover all areas with k candidates\n", + "\n", + " return team\n", + "\n", + "# Example usage\n", + "expertiseAreas = {'biology', 'chemistry', 'physics', 'mathematics', 'computer science'}\n", + "candidates = [\n", + " {'name': 'Alice', 'expertise': {'biology', 'chemistry'}},\n", + " {'name': 'Bob', 'expertise': {'physics', 'mathematics'}},\n", + " {'name': 'Charlie', 'expertise': {'computer science'}},\n", + " {'name': 'Dana', 'expertise': {'biology', 'computer science'}}\n", + "]\n", + "k = 3\n", + "\n", + "optimal_team = greedyOptimalResearchTeam(expertiseAreas, candidates, k)\n", + "if optimal_team:\n", + " for member in optimal_team:\n", + " print(member['name'])\n", + "else:\n", + " print(\"No solution found\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hLYh-jHDBYxS", + "outputId": "2df82b0c-fe75-45d1-98c9-feabca3dbc41" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Alice\n", + "Bob\n", + "Charlie\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Analysis**\n", + "\n", + "* **Performance:** The greedy algorithm is much faster than the brute-force approach, typically running in O(nk) time where n is the number of candidates, and k is the maximum team size.\n", + "\n", + "* **Accuracy:** This method doesn't always find the optimal solution, especially in cases where selecting a candidate with fewer expertise areas initially could lead to a better overall team composition.\n", + "\n", + "* **Use Cases:** It's a good choice when an approximate solution is acceptable or when the input size makes the brute-force approach impractical.\n", + "\n", + "For very large datasets or more complex requirements, other methods like dynamic programming (if applicable), integer linear programming, or even heuristic methods like genetic algorithms might be more suitable, but they come with their own complexities and trade-offs." + ], + "metadata": { + "id": "oL7aDlkwBfOo" + } + }, + { + "cell_type": "markdown", + "source": [ + "## **Q4:**\n", + "\n", + "**Efficient Resource Allocation in a Tech Company**\n", + "\n", + "Suppose you're a project manager at a tech company, and you're faced with the following challenge. The company has several ongoing projects, each requiring expertise in different areas (like software development, data analysis, UI/UX design, etc.). There are p projects and the company has a pool of q potential employees. Each employee has a specific set of skills, and each project requires at least one person skilled in each of its required areas.\n", + "\n", + "The question is: For a given number r < q, is it possible to assign at most r employees in such a way that all p projects have at least one person skilled in each required area? This problem is referred to as the Efficient Resource Allocation Problem.\n", + "\n", + "\n", + "### **Solution:**\n", + "\n", + "To show that the Efficient Resource Allocation Problem (ERAP) is NP-complete, we need to prove two things:\n", + "\n", + "1. **ERAP is in NP:** This means that given a particular assignment of employees to projects, we can verify in polynomial time whether each project has at least one person skilled in each required area.\n", + "\n", + "2. **ERAP is NP-hard:** This involves reducing a known NP-complete problem to ERAP in polynomial time, demonstrating that ERAP is at least as hard as the known NP-complete problem.\n", + "\n", + "#### **ERAP is in NP**\n", + "\n", + "To prove that ERAP is in NP, we consider a \"certificate\" or a solution, which in this case is the assignment of employees to projects. Given this assignment, we can easily check in polynomial time whether each project has at least one employee skilled in each of its required areas by simply going through the list of projects and checking the skills of the assigned employees. This process is clearly polynomial in terms of the number of projects and employees, thus ERAP is in NP.\n", + "\n", + "#### **ERAP is NP-hard**\n", + "To prove NP-hardness, we typically reduce a known NP-complete problem to ERAP. A suitable candidate for this is the Set Cover problem, which is known to be NP-complete. The Set Cover problem is stated as follows: Given a universal set U, a collection of subsets S of U, and an integer k, the question is whether there are k or fewer subsets in S whose union equals U.\n", + "\n", + "Reduction from Set Cover to ERAP\n", + "1. **Mapping Instances:** Each element in the universal set U in Set Cover can be thought of as a \"project\" in ERAP. Each subset in S corresponds to an \"employee\" in ERAP, where the elements in the subset represent the skills of the employee.\n", + "\n", + "2. **Mapping Solutions:** If we can cover the universal set U with k subsets in the Set Cover problem, this means we can assign k employees in ERAP such that all projects are covered.\n", + "\n", + "3. **Polynomial Time Reduction:** This mapping can be done in polynomial time since it involves a straightforward conversion of elements and subsets to projects and employees, respectively.\n", + "\n", + "#### **Conclusion**\n", + "\n", + "Since we can reduce the Set Cover problem to ERAP in polynomial time, and the Set Cover problem is NP-complete, ERAP is NP-hard. Combining this with the fact that ERAP is in NP, we conclude that ERAP is NP-complete.\n", + "\n", + "This proof demonstrates that solving ERAP is computationally challenging, as it belongs to a class of problems for which no polynomial-time solutions are known. The implication for a project manager in a tech company is significant: finding the most efficient resource allocation might require exploring a vast number of possibilities, especially as the number of projects and employees grows.\n", + "\n", + "To provide a more detailed step-by-step solution to demonstrate that the Efficient Resource Allocation Problem (ERAP) is NP-complete, we'll break down the process into a more granular explanation.\n", + "\n", + "#### **Step 1: Understanding ERAP**\n", + "\n", + "1. **Definition of ERAP:** In the ERAP, you have p projects and q potential employees. Each project requires specific skills, and each employee has a unique set of skills. The question is whether it's possible to assign at most r employees (where r < q) such that all projects have at least one person skilled in each required area.\n", + "\n", + "#### **Step 2: Proving ERAP is in NP**\n", + "\n", + "1. **Verifying a Solution is Polynomial:** Given a specific assignment of r employees to p projects, we need to verify if each project has at least one employee with the required skills. We do this by iterating over each project and checking if the assigned employees cover all the required skills. This verification can be done in polynomial time with respect to the number of projects and employees.\n", + "\n", + "#### **Step 3: Proving ERAP is NP-hard**\n", + "1. **Choosing a Known NP-complete Problem:** We choose the Set Cover problem, known to be NP-complete. In Set Cover, given a universal set U, a collection of subsets S, and an integer k, the task is to determine whether there are k or fewer subsets in S whose union equals U.\n", + "\n", + "2. **Mapping Set Cover to ERAP:**\n", + "\n", + " * Elements to Projects: Elements of the universal set U in Set Cover are analogous to projects in ERAP.\n", + " * Subsets to Employees: Each subset in S represents an employee. The elements in the subset signify the skills of the employee.\n", + " * Set Cover to Employee Assignment: If k subsets in Set Cover can cover the universal set U, it corresponds to assigning k employees in ERAP to cover all projects.\n", + "\n", + "3. **Demonstrating Polynomial Time Reduction:** The mapping from Set Cover to ERAP should be polynomial in time. This is evident as it involves direct correspondence between elements of U (projects) and subsets in S (employees), and no complex computations are needed.\n", + "\n", + "4. **Implication of the Reduction:** By showing that every instance of the Set Cover problem can be transformed into an instance of ERAP, and since solving the Set Cover problem is as hard as solving any problem in NP (as it's NP-complete), it implies that solving ERAP is at least as hard as solving any problem in NP.\n", + "\n", + "#### **Conclusion: ERAP is NP-complete**\n", + "By combining the proof that ERAP is in NP (easy to verify a solution) and NP-hard (at least as hard as any problem in NP, demonstrated by the reduction from Set Cover), we conclude that ERAP is NP-complete. This means that there is no known polynomial-time solution for ERAP, and it is as hard to solve as the hardest problems in NP. For a project manager, this complexity suggests that finding an optimal solution might be computationally intensive, especially as the number of projects and employees increases." + ], + "metadata": { + "id": "eUeQSL1UBfFe" + } + }, + { + "cell_type": "markdown", + "source": [ + "Solving the Efficient Resource Allocation Problem (ERAP), which is NP-complete, optimally is generally computationally infeasible for large instances due to its complexity. However, there are several algorithmic approaches one can take to find feasible, if not optimal, solutions:\n", + "\n", + "1. **Brute Force Algorithm:** This involves trying all possible combinations of employees and projects, but it's impractical for large numbers of employees and projects due to its exponential time complexity.\n", + "\n", + "2. **Greedy Algorithms:** These algorithms make the locally optimal choice at each stage. For ERAP, a greedy approach might involve assigning the most skilled (or versatile) employees first or prioritizing projects with the rarest required skills. However, greedy algorithms do not guarantee an optimal solution.\n", + "\n", + "3. **Backtracking:** This is a refined brute force approach, where you systematically search for a solution by trying to build a solution incrementally, abandoning a path as soon as it is determined that this path cannot possibly lead to a solution.\n", + "\n", + "4. **Approximation Algorithms:** For NP-complete problems, approximation algorithms are often used. They provide solutions that are close to optimal within a provable bound.\n", + "\n", + "5. **Heuristic Methods:** Algorithms like Genetic Algorithms, Simulated Annealing, or other evolutionary algorithms can be used. They don't guarantee an optimal solution but can often find good solutions with less computational effort than exact algorithms.\n", + "\n", + "6. **Integer Linear Programming (ILP):** While ILP is generally NP-hard, modern solvers are very effective at solving large instances of ILPs in practice. You can model ERAP as an ILP and use a solver like Gurobi or CBC.\n", + "\n", + "Let's create a simple example using a backtracking approach in Python. This code will try to assign employees to projects while ensuring that each project has the required skills. Note that this is a basic implementation and may not be efficient for large instances:" + ], + "metadata": { + "id": "kQXz6BG3G6iK" + } + }, + { + "cell_type": "code", + "source": [ + "# Adjusted input data\n", + "projects = [[\"software development\", \"data analysis\"], [\"UI/UX design\", \"software development\"]]\n", + "employees = [[\"software development\", \"data analysis\"], [\"UI/UX design\"], [\"software development\", \"UI/UX design\"]]\n", + "\n", + "# The same backtracking function as before\n", + "def is_valid_assignment(projects, employees, assignment, project_idx):\n", + " required_skills = projects[project_idx]\n", + " for skill in required_skills:\n", + " if not any(skill in employees[emp] for emp in assignment):\n", + " return False\n", + " return True\n", + "\n", + "def assign_employees(projects, employees, assignment=[], project_idx=0):\n", + " if project_idx == len(projects):\n", + " return assignment # All projects are assigned successfully\n", + "\n", + " for i in range(len(employees)):\n", + " if i not in assignment:\n", + " assignment.append(i)\n", + " if is_valid_assignment(projects, employees, assignment, project_idx):\n", + " result = assign_employees(projects, employees, assignment, project_idx + 1)\n", + " if result is not None:\n", + " return result\n", + " assignment.pop()\n", + "\n", + " return None\n", + "\n", + "# Example usage\n", + "assignment = assign_employees(projects, employees)\n", + "print(\"Assignment:\", assignment)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GDv6OncXHKzf", + "outputId": "c954eb22-c1c8-4e71-cffe-a1599a183b7c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Assignment: [0, 1]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "This script defines a basic backtracking solution where projects is a list of lists containing the required skills for each project, and employees is a list of lists containing the skills of each employee. The function assign_employees tries to find an assignment of employees to projects where all project requirements are met. If it finds such an assignment, it returns it; otherwise, it returns None." + ], + "metadata": { + "id": "_cj7KcYlHd68" + } + }, + { + "cell_type": "markdown", + "source": [ + "##**Q5:**\n", + "\n", + "Imagine you are part of a study group of n members who plan to organize n study sessions over n days. Each member has to lead a session on one of these days, ensuring that there is one leader for each day.\n", + "\n", + "Like the cooking scenario, every member has conflicts on certain days (like lab sessions, concerts, etc.), meaning they can't lead the study session on those days. Let's label the group members $G ∈ {g_1, ..., g_n}$ and the days $D ∈ {d_1, ..., d_n}$. For each group member gi, there's a set of days $T_i ⊂ {d_1, ..., d_n}$ when they cannot lead the session. No member can have $T_i$ empty.\n", + "\n", + "If a member isn't scheduled to lead a session on any of the n days, they must contribute $50 towards group study materials.\n", + "\n", + "A. Frame this problem as a maximum flow problem that schedules the maximum number of leader-day pairings.\n", + "\n", + "B. Can all n group members be matched with one of the n days? Provide a proof or counter-argument to support whether this is always possible or not.\n", + "\n", + "### **Solution:**\n", + "\n", + "To answer the question regarding the scheduling of study sessions (or cooking days, as in your original question), we can model this as a maximum flow problem. Let's go through the solution step by step:\n", + "\n", + "#### **1. Representing the Problem as a Graph**\n", + "\n", + "We create a directed graph with the following components:\n", + "\n", + "* A source node S.\n", + "* A sink node T.\n", + "* Nodes representing each group member Gi (for i = 1 to n).\n", + "* Nodes representing each day Di (for i = 1 to n).\n", + "\n", + "The edges are as follows:\n", + "\n", + "* An edge from S to each Gi with a capacity of 1 (each member leads one session).\n", + "* An edge from each Di to T with a capacity of 1 (each day has one session).\n", + "* An edge from Gi to Dj if and only if member Gi is available to lead on day Dj.\n", + "\n", + "#### **2. Finding the Maximum Flow**\n", + "\n", + "The goal is to find the maximum flow in this graph, which corresponds to the maximum number of leader-day pairings. We can use the Ford-Fulkerson algorithm or its variation like the Edmonds-Karp algorithm.\n", + "\n", + "Pseudo-code for Ford-Fulkerson:\n", + "\n", + "*** *Attention: it's pseudocode for better explanation. It's not runnable.***" + ], + "metadata": { + "id": "hhHrt6_jHWMs" + } + }, + { + "cell_type": "code", + "source": [ + "def ford_fulkerson(graph, source, sink):\n", + " max_flow = 0\n", + " while there is a path from source to sink in residual graph:\n", + " path_flow = find_minimum_capacity_on_path(path)\n", + " max_flow += path_flow\n", + " update_residual_graph(graph, path, path_flow)\n", + " return max_flow" + ], + "metadata": { + "id": "9AJZ354cPgpL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#### **3. Matching Group Members with Days**\n", + "The maximum flow value gives us the number of leader-day pairings. If this value is n, it means each member is matched with a unique day.\n", + "\n", + "#### **4. Can All Members Always Be Matched?**\n", + "Now, to answer whether all n members can always be matched with one of the n days, we need to examine the nature of the graph:\n", + "\n", + "* If the graph has a perfect matching (i.e., the maximum flow is n), then each member can be matched with a day.\n", + "* If the graph does not have a perfect matching (i.e., the maximum flow is less than n), then not all members can be matched.\n", + "\n", + "#### **5. Proof or Counter-Argument**\n", + "* **Hall's Marriage Theorem** can be applied here. It states that if for every subset of group members, the number of days they can lead is at least as large as the number of members in the subset, then a complete matching exists.\n", + "* If there's any subset of members for which the available days are fewer than the members, then not all members can be matched.\n", + "\n", + "#### **A. Framing as a Maximum Flow Problem**\n", + "\n", + "1. Constructing the Flow Network:\n", + "\n", + "* **Source and Sink:** Introduce a source node (S) and a sink node (T).\n", + "* **Group Member Nodes:** For each group member $g_i$, create a node.\n", + "* **Day Nodes:** For each day $d_j$, create a node.\n", + "* **Edges from Source to Group Members:** Connect the source (S) to each group member node with an edge of capacity 1. This represents that each member can lead only one session.\n", + "* **Edges from Group Members to Days:** Connect each group member node $g_i$ to the day nodes $d_j$, except for the days they are unavailable (as per $T_i$). Each edge has a capacity of 1, indicating that a member can lead on a day only once.\n", + "* **Edges from Days to Sink:** Connect each day node to the sink (T) with an edge of capacity 1. This represents that each day can have only one leader.\n", + "\n", + "2. Flow Maximization:\n", + "\n", + "* **Objective:** Maximize the flow from S to T. Each unit of flow represents a leader-day pairing.\n", + "* **Constraints:** Flow conservation at each node (except S and T) and capacity constraints on the edges.\n", + "\n", + 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)\n", + "\n", + "B. Can All $n$ Members Be Matched?\n", + "\n", + "1. **Analysis:**\n", + "\n", + "* **Hall's Marriage Theorem:** This situation can be analyzed using Hall's Marriage Theorem, which states that a perfect matching exists if and only if for every subset $S$ of the group members, the number of days available to the members in $S$ is at least as large as the size of $S$.\n", + "* **Applicability to Our Problem:** In this scenario, the theorem implies that a perfect match (where each group member leads on one day) is possible if and only if for any subset of group members, there are at least as many days collectively available to them as there are members in the subset.\n", + "\n", + "2. **Proof or Counter-Argument:**\n", + "\n", + "* **Proof (If Applicable):** If it can be shown that for every subset of group members the number of available days is at least as large as the subset, then according to Hall's theorem, a perfect matching exists.\n", + "\n", + "* **Counter-Argument (If Applicable):** If there exists a subset of group members for which the number of collectively available days is less than the size of the subset, then a perfect matching is not possible.\n", + "\n", + "3. **Conclusion:**\n", + "\n", + " Whether all $n$ members can be matched with one of the $n$ days depends on the distribution of their unavailable days. If the condition set by Hall's theorem is met for all subsets, then a perfect match is possible. If not, then it's impossible to schedule each member to lead a session without conflicts.\n", + "\n" + ], + "metadata": { + "id": "MLt8uIKDPj7V" + } + }, + { + "cell_type": "markdown", + "source": [ + "To create a Python solution for this problem, we can use a maximum flow algorithm. Given that we do not have the specific details about each group member's unavailable days, I'll create a general framework where you can input these details. We'll use the networkx library for creating and analyzing the flow network.\n", + "\n", + "First, let's outline the steps the code will follow:\n", + "\n", + "1. **Create a Flow Network:** We'll set up a graph with source and sink nodes, nodes for each group member and day, and edges based on their availability.\n", + "2. **Apply a Maximum Flow Algorithm:** Use an algorithm like Ford-Fulkerson to find the maximum number of leader-day pairings.\n", + "3. **Analyze the Result:** Determine if all members are matched with a day.\n", + "\n", + "Here's a Python script skeleton for this approach:" + ], + "metadata": { + "id": "3wwGSCo9a_kq" + } + }, + { + "cell_type": "code", + "source": [ + "import networkx as nx\n", + "\n", + "def create_flow_network(n, member_unavailability):\n", + " # Create a directed graph\n", + " G = nx.DiGraph()\n", + "\n", + " # Add source (S) and sink (T) nodes\n", + " G.add_node(\"S\")\n", + " G.add_node(\"T\")\n", + "\n", + " # Add nodes and edges for group members and days\n", + " for i in range(1, n + 1):\n", + " member_node = f'g{i}'\n", + " day_node = f'd{i}'\n", + "\n", + " # Add nodes\n", + " G.add_node(member_node)\n", + " G.add_node(day_node)\n", + "\n", + " # Add edges from source to group members\n", + " G.add_edge(\"S\", member_node, capacity=1)\n", + "\n", + " # Add edges from days to sink\n", + " G.add_edge(day_node, \"T\", capacity=1)\n", + "\n", + " # Add edges from group members to available days\n", + " for j in range(1, n + 1):\n", + " if j not in member_unavailability[i - 1]:\n", + " G.add_edge(member_node, f'd{j}', capacity=1)\n", + "\n", + " return G\n", + "\n", + "def maximum_matching(n, member_unavailability):\n", + " G = create_flow_network(n, member_unavailability)\n", + " flow_value, flow_dict = nx.maximum_flow(G, \"S\", \"T\")\n", + " return flow_value, flow_dict\n", + "\n", + "# Example usage\n", + "n = 4 # Number of members and days\n", + "member_unavailability = [\n", + " [1, 2], # Member 1 is unavailable on days 1 and 2\n", + " [2, 3], # Member 2 is unavailable on days 2 and 3\n", + " [3, 4], # Member 3 is unavailable on days 3 and 4\n", + " [1, 4] # Member 4 is unavailable on days 1 and 4\n", + "]\n", + "\n", + "# Calculate the maximum matching\n", + "flow_value, flow_dict = maximum_matching(n, member_unavailability)\n", + "\n", + "# Check if all members can be matched\n", + "all_matched = flow_value == n\n", + "\n", + "print(f\"All members matched: {all_matched}\")\n", + "print(\"Flow dict:\", flow_dict)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xY35hIqma235", + "outputId": "b0b2d6ad-eaf9-4622-b6b0-126f3c798a88" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "All members matched: True\n", + "Flow dict: {'S': {'g1': 1, 'g2': 1, 'g3': 1, 'g4': 1}, 'T': {}, 'g1': {'d3': 1, 'd4': 0}, 'd1': {'T': 1}, 'd3': {'T': 1}, 'd4': {'T': 1}, 'g2': {'d1': 0, 'd4': 1}, 'd2': {'T': 1}, 'g3': {'d1': 1, 'd2': 0}, 'g4': {'d2': 1, 'd3': 0}}\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "In this code, member_unavailability is a list of lists, where each inner list contains the days when the corresponding member is unavailable. You can modify this list based on the actual availability of group members. You can adjust the member_unavailability list according to the specific scenario to get the correct answer.\n", + "\n", + "\n" + ], + "metadata": { + "id": "g7nK4OulbJ3O" + } + }, + { + "cell_type": "markdown", + "source": [ + "## **Q6:**\n", + "\n", + "Consider an extension of the Max-SAT problem called Prob-Max-SAT, which involves maximizing the probability of satisfying clauses under certain constraints. Prob-Max-SAT is defined as follows: Given a set of $m$ clauses $D = {D_1, D_2, ..., D_m}$ and $p$ literals $Y = {Y_1, Y_2, ..., Y_p}$, each with an associated probability of being true, find a truth assignment that maximizes the expected number of satisfied clauses. Each clause must contain at least one literal, and all literals within a clause are unique. The probabilities of literals being true are independent of each other.\n", + "\n", + "A. Calculate the expected number of satisfied clauses if each clause contains exactly two literals, and each literal has a 70% chance of being true.\n", + "\n", + "B. If each clause contains exactly two literals, is there a strategy that guarantees satisfaction of all m clauses?\n", + "\n", + "C. Develop a randomized polynomial-time algorithm for Prob-Max-SAT where each clause can have between 1 and p literals, aiming to satisfy at least 60% of the m clauses. Consider that the probability of each literal being true varies between 50% and 80%.\n", + "\n", + "### **Answer:**\n", + "\n", + "#### **Question Analysis**\n", + "\n", + "The question deals with Prob-Max-SAT, a variation of the Max-SAT problem. It involves finding truth assignments to maximize the expected number of satisfied clauses. Each literal has a probability of being true.\n", + "\n", + "**Part A: Expected Number of Satisfied Clauses**\n", + "\n", + "Problem: Each clause contains exactly two literals, each with a 70% chance of being true. We need to calculate the expected number of satisfied clauses.\n", + "\n", + "**Solution:**\n", + "\n", + "A clause with two literals is satisfied if at least one literal is true. The probability of a clause being satisfied is the complement of the probability that both literals are false.\n", + "\n", + "* Probability that a literal is false = 1 - 0.7 = 0.3\n", + "* Probability that both literals in a clause are false = 0.3 * 0.3 = 0.09\n", + "* Probability that a clause is satisfied = 1 - 0.09 = 0.91\n", + "\n", + "If there are m clauses, the expected number of satisfied clauses = 0.91 * m.\n", + "\n", + "**Part B: Guaranteeing Satisfaction of All Clauses**\n", + "\n", + "Problem: Determine if there's a strategy to always satisfy all m clauses when each clause contains exactly two literals.\n", + "\n", + "**Solution:**\n", + "\n", + "If each clause has two literals, there is no strategy that guarantees the satisfaction of all clauses. The satisfaction of clauses depends on the truth assignment of the literals, which is probabilistic and cannot be controlled or predicted with certainty.\n", + "\n", + "**Part C: Randomized Polynomial-time Algorithm**\n", + "\n", + "Problem: Develop a randomized algorithm for Prob-Max-SAT to satisfy at least 60% of the m clauses, where each clause can have between 1 and p literals, and the probability of each literal being true varies between 50% and 80%.\n", + "\n", + "**Solution:**\n", + "\n", + "The algorithm involves assigning truth values to literals based on their probability of being true. The higher the probability, the more likely we assign 'true' to the literal.\n", + "\n", + "**Pseudo-code:**\n", + "\n", + "*** *Attention: it's pseudocode for better explanation. It's not runnable.***" + ], + "metadata": { + "id": "HyGKtsccVNEU" + } + }, + { + "cell_type": "code", + "source": [ + "import random\n", + "\n", + "def assign_truth_value(probability):\n", + " return random.random() < probability\n", + "\n", + "def prob_max_sat(clauses, literals_probabilities):\n", + " truth_assignments = {literal: assign_truth_value(prob) for literal, prob in literals_probabilities.items()}\n", + " satisfied_clauses = 0\n", + "\n", + " for clause in clauses:\n", + " if any(truth_assignments[literal] for literal in clause):\n", + " satisfied_clauses += 1\n", + "\n", + " return satisfied_clauses\n", + "\n", + "# Example Usage\n", + "# clauses = [['Y1', 'Y2'], ['Y2', 'Y3'], ...] # List of clauses\n", + "# literals_probabilities = {'Y1': 0.7, 'Y2': 0.8, ...} # Probabilities of literals\n", + "# satisfied_count = prob_max_sat(clauses, literals_probabilities)" + ], + "metadata": { + "id": "JeyPyJ2ueOeA" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "This algorithm iterates through each clause and checks if any of its literals are true based on the assigned truth values. The expected performance of this algorithm should satisfy at least 60% of the clauses, given the probabilities of literals being true are reasonably high (between 50% and 80%).\n", + "\n", + "The complexity of this algorithm is polynomial, as it iterates over each clause once and checks the truth value of each literal within those clauses.\n", + "\n", + "#### **Python Code Implementation**\n", + "\n", + "The code will include:\n", + "\n", + "1. A function to randomly assign truth values to literals based on their given probabilities.\n", + "2. A function to evaluate the number of satisfied clauses in the Prob-Max-SAT problem.\n", + "3. An example setup of clauses and literal probabilities to test the algorithm." + ], + "metadata": { + "id": "1irWCo71eRSr" + } + }, + { + "cell_type": "code", + "source": [ + "import random\n", + "\n", + "def assign_truth_value(probability):\n", + " \"\"\"Assign a truth value to a literal based on its probability of being true.\"\"\"\n", + " return random.random() < probability\n", + "\n", + "def prob_max_sat(clauses, literals_probabilities):\n", + " \"\"\"Evaluate the number of satisfied clauses in the Prob-Max-SAT problem.\"\"\"\n", + " truth_assignments = {literal: assign_truth_value(prob) for literal, prob in literals_probabilities.items()}\n", + " satisfied_clauses = 0\n", + "\n", + " for clause in clauses:\n", + " if any(truth_assignments[literal] for literal in clause):\n", + " satisfied_clauses += 1\n", + "\n", + " return satisfied_clauses\n", + "\n", + "# Example Setup\n", + "clauses = [['Y1', 'Y2'], ['Y2', 'Y3'], ['Y1', 'Y3']] # Example list of clauses\n", + "literals_probabilities = {'Y1': 0.7, 'Y2': 0.5, 'Y3': 0.8} # Probabilities of literals\n", + "\n", + "# Run the algorithm\n", + "satisfied_count = prob_max_sat(clauses, literals_probabilities)\n", + "print(f\"Number of satisfied clauses: {satisfied_count} out of {len(clauses)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4cXZugTKe52i", + "outputId": "90e25443-b8d0-46d0-c031-cfde5e509766" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Number of satisfied clauses: 3 out of 3\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "This code provides a basic implementation. In real-world applications, you might need to run the algorithm multiple times and take averages, due to the probabilistic nature of the assignments." + ], + "metadata": { + "id": "tFWIt-Pge98w" + } + }, + { + "cell_type": "markdown", + "source": [ + "## **Q7:**\n", + "\n", + "**The Subset Sum Problem**\n", + "\n", + "The Subset Sum problem involves determining if there exists a subset within a given set of integers that sums up to a specific target value. Consider a set $T$ of integers, both positive and negative, and a target sum $Z$.\n", + "\n", + "For instance, given T = {4, -1, 3, 2, -2} and Z = 0, one valid solution to the Subset Sum problem is finding a subset {4, -1, -2, -1} whose elements add up to 0. Note that solutions can vary, and the presence of negative numbers adds complexity to the problem.\n", + "\n", + "Questions:\n", + "\n", + "A. Is the Subset Sum problem in NP? Provide a rationale for your answer.\n", + "\n", + "B. Assess whether the Subset Sum problem is NP-complete. If it is NP-complete, demonstrate this through a proof.\n", + "\n", + "\n", + "### **Answer:**\n", + "\n", + "#### **A. Is the Subset Sum Problem in NP?**\n", + "\n", + "**Answer:** Yes, the Subset Sum problem is in NP (Non-deterministic Polynomial time).\n", + "\n", + "**Explanation:**\n", + "\n", + "* The class NP is characterized by problems for which a given solution can be verified in polynomial time.\n", + "\n", + "* For the Subset Sum problem, if we are given a subset of T, we can verify whether the sum of its elements equals Z in polynomial time. This verification process is straightforward: sum up the elements of the subset and check if the sum equals Z.\n", + "\n", + "* The verification process can be represented in pseudocode as follows:\n", + "\n", + "*** *Attention: it's pseudocode for better explanation. It's not runnable.***\n", + "\n" + ], + "metadata": { + "id": "aDjpsggLYEf6" + } + }, + { + "cell_type": "code", + "source": [ + "def verify_subset_sum(subset, target_sum):\n", + " if sum(subset) == target_sum:\n", + " return True\n", + " else:\n", + " return False" + ], + "metadata": { + "id": "DtwZiLOmgVqq" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#### **B. Is the Subset Sum Problem NP-complete?**\n", + "\n", + "**Answer:** Yes, the Subset Sum problem is NP-complete.\n", + "\n", + "**Explanation:**\n", + "\n", + "1. **Subset Sum is in NP:** As established above, any proposed solution (a subset of T) can be verified in polynomial time.\n", + "\n", + "2. **Subset Sum is NP-hard:** To prove this, we can show that an already known NP-complete problem can be polynomially reduced to the Subset Sum problem. One such problem is the 3-SAT problem.\n", + "\n", + "3. **Reduction from 3-SAT to Subset Sum:**\n", + "\n", + "* 3-SAT Problem: Given a boolean formula with clauses having exactly three literals, determine if there's a truth assignment to the variables that makes the entire formula true.\n", + "* We can construct a polynomial-time reduction from 3-SAT to Subset Sum such that the formula is satisfiable if and only if there exists a subset summing up to a specific target in the constructed set.\n", + "* This reduction process involves encoding the clauses and variables of the 3-SAT problem into numbers in a way that the sum reflects the satisfaction of clauses under a certain truth assignment.\n", + "* Due to the complexity of this reduction, detailed pseudocode isn't trivial and is typically subject matter for advanced algorithmic or computational complexity courses.\n", + "\n", + "4. **Conclusion:**\n", + "\n", + "* Since the Subset Sum problem is in NP and any instance of an NP-complete problem can be polynomially reduced to it, Subset Sum is NP-complete." + ], + "metadata": { + "id": "tQS7aoncgZDB" + } + }, + { + "cell_type": "markdown", + "source": [ + "Here's a Python function to determine if there's a subset within a given set of integers (numbers) that sums up to a specific target value (target_sum):" + ], + "metadata": { + "id": "B-zpDUishMJr" + } + }, + { + "cell_type": "code", + "source": [ + "def is_subset_sum(numbers, target_sum):\n", + " n = len(numbers)\n", + " # Using a hashmap to store reachable sums\n", + " sums = {0: True}\n", + "\n", + " for num in numbers:\n", + " current_sums = list(sums.keys())\n", + " for s in current_sums:\n", + " new_sum = s + num\n", + " if new_sum == target_sum:\n", + " return True\n", + " sums[new_sum] = True\n", + "\n", + " return target_sum in sums\n", + "\n", + "# Example usage\n", + "numbers = [4, -1, 3, 2, -2]\n", + "target_sum = 0\n", + "result = is_subset_sum(numbers, target_sum)\n", + "print(\"Subset with given sum exists.\" if result else \"No subset with given sum exists.\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Xc_Q32EAhOOn", + "outputId": "a01ab182-06e1-4536-ed42-102c3dc14faf" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Subset with given sum exists.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "This code defines a function is_subset_sum which takes a list of numbers and a target sum. It uses dynamic programming to build up a table dp where each entry dp[i][j] indicates whether there is a subset of the first i numbers that sums up to j. The function returns True if such a subset exists and False otherwise." + ], + "metadata": { + "id": "lw0SGoBuhWGq" + } + }, + { + "cell_type": "markdown", + "source": [ + "## **Q8:**\n", + "\n", + "**SEMESTER COURSE SELECTION CHALLENGE:**\n", + "\n", + "You are a student advisor at XYZ University, tasked with assisting students in planning their course schedules for the upcoming Fall semester. The university offers a total of n1 different courses.\n", + "\n", + "You have a list of n2 students, each with their own unique set of course preferences and prerequisites they have already completed.\n", + "\n", + "There are two ways you can gather information about each course to provide effective advice:\n", + "\n", + "1. Audit the course yourself, which requires attending one full lecture.\n", + "2. Consult with a student who has already taken the course, which takes one hour per student.\n", + "Each auditing session or consultation takes one hour of your time.\n", + "\n", + "**GOAL OF THE CHALLENGE:**\n", + "\n", + "Your objective is to gain sufficient knowledge about each course to confidently advise students on their course selection. You determine that you can adequately understand a course by either auditing it or consulting with at least one student who has previously taken it.\n", + "\n", + "You can dedicate a total of k hours to this task before the advising period begins.\n", + "\n", + "Your challenge is to develop a strategy to use your k hours most efficiently. Create a polynomial-time algorithm to select the optimal combination of courses to audit and students to consult. Alternatively, if you believe this problem is computationally complex, provide a justification for why it is NP-hard.\n", + "\n", + "### **Answer:**\n", + "\n", + "\n", + "This problem is essentially a variant of the Set Cover problem, which is a well-known NP-hard problem in computer science. The Set Cover problem involves finding the smallest subset of sets that covers all elements in a universal set. Translating this to your problem, the universal set is the set of all courses (n1 courses), and the subsets are the sets of courses each student has taken or the individual courses you can audit.\n", + "\n", + "To solve this problem within the constraints of k hours, you need to pick a combination of courses to audit and students to consult so that all courses are covered while minimizing the time spent (not exceeding k hours).\n", + "\n", + "#### **Step-by-Step Solution:**\n", + "\n", + "1. Representation of Data:\n", + "\n", + "* Let each course be represented by a unique identifier.\n", + "* Each student is associated with a set of courses they have taken.\n", + "\n", + "2. Selecting Courses and Students:\n", + "\n", + "* Initially, no courses are covered.\n", + "* You need to select combinations of auditing courses and consulting students such that all courses are eventually covered.\n", + "\n", + "3. Greedy Approach:\n", + "\n", + "* The problem can be approached using a greedy algorithm, although it won't guarantee the optimal solution due to its NP-hard nature.\n", + "* At each step, choose the action (auditing a course or consulting a student) that covers the maximum number of uncovered courses.\n", + "\n", + "4. Pseudo-code:" + ], + "metadata": { + "id": "4oup7riGfrrk" + } + }, + { + "cell_type": "code", + "source": [ + "def select_courses_and_students(courses, students, k):\n", + " covered_courses = set()\n", + " selected_actions = []\n", + "\n", + " while len(covered_courses) < len(courses) and k > 0:\n", + " # Find the action that covers the most uncovered courses\n", + " best_action = None\n", + " max_covered = 0\n", + "\n", + " # Check each course for auditing\n", + " for course in courses:\n", + " if course not in covered_courses and len(course) > max_covered:\n", + " best_action = ('audit', course)\n", + " max_covered = len(course)\n", + "\n", + " # Check each student for consultation\n", + " for student in students:\n", + " covered_by_student = set(student.courses).difference(covered_courses)\n", + " if len(covered_by_student) > max_covered:\n", + " best_action = ('consult', student)\n", + " max_covered = len(covered_by_student)\n", + "\n", + " # Add the best action to the selected actions\n", + " if best_action:\n", + " selected_actions.append(best_action)\n", + " if best_action[0] == 'audit':\n", + " covered_courses.add(best_action[1])\n", + " else:\n", + " covered_courses.update(best_action[1].courses)\n", + " k -= 1\n", + " else:\n", + " break\n", + "\n", + " return selected_actions" + ], + "metadata": { + "id": "eVLuEG8GmYgq" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "5. Analysis:\n", + "\n", + "* This algorithm tries to maximize the number of newly covered courses at each step within the limit of k hours.\n", + "* However, because of its greedy nature, it may not always provide the optimal solution.\n", + "\n", + "6. NP-hard Justification:\n", + "\n", + "* This problem is NP-hard because it is a variation of the Set Cover problem, which is known to be NP-hard. There's no known polynomial-time algorithm to find the optimal solution for all instances of such problems.\n", + "* In NP-hard problems, verifying a given solution is feasible in polynomial time, but finding the best solution might require an impractical amount of time for large datasets.\n", + "\n", + "#### **Conclusion:**\n", + "This approach provides a feasible solution but not necessarily the optimal one due to the NP-hard nature of the problem. Advanced techniques or heuristics might be needed for more efficient solutions, especially for larger instances of the problem." + ], + "metadata": { + "id": "UAVtKM09mc9R" + } + }, + { + "cell_type": "markdown", + "source": [ + "To provide a runnable Python code for the problem, we'll simplify the approach and assumptions a bit. We'll assume that you have data structures that represent the courses each student has taken. The goal is to cover all the courses by either auditing them or consulting with students, within the limit of k hours.\n", + "\n", + "#### **Python Implementation**\n" + ], + "metadata": { + "id": "B9TLp-0zm8Ev" + } + }, + { + "cell_type": "code", + "source": [ + "class Student:\n", + " def __init__(self, id, courses):\n", + " self.id = id\n", + " self.courses = set(courses)\n", + "\n", + "def select_courses_and_students(courses, students, k):\n", + " covered_courses = set()\n", + " selected_actions = []\n", + "\n", + " while len(covered_courses) < len(courses) and k > 0:\n", + " best_action = None\n", + " max_covered = 0\n", + "\n", + " # Check each course for auditing\n", + " for course in courses:\n", + " if course not in covered_courses and 1 > max_covered:\n", + " best_action = ('audit', course)\n", + " max_covered = 1\n", + "\n", + " # Check each student for consultation\n", + " for student in students:\n", + " covered_by_student = student.courses.difference(covered_courses)\n", + " if len(covered_by_student) > max_covered:\n", + " best_action = ('consult', student)\n", + " max_covered = len(covered_by_student)\n", + "\n", + " # Add the best action to the selected actions\n", + " if best_action:\n", + " selected_actions.append(best_action)\n", + " if best_action[0] == 'audit':\n", + " covered_courses.add(best_action[1])\n", + " else:\n", + " covered_courses.update(best_action[1].courses)\n", + " k -= 1\n", + " else:\n", + " break\n", + "\n", + " return selected_actions\n", + "\n", + "# Example Usage\n", + "courses = {'CS101', 'CS102', 'CS103', 'CS104'}\n", + "students = [Student(1, {'CS101', 'CS102'}), Student(2, {'CS102', 'CS103'}), Student(3, {'CS103', 'CS104'})]\n", + "\n", + "selected_actions = select_courses_and_students(courses, students, 3)\n", + "for action in selected_actions:\n", + " if action[0] == 'audit':\n", + " print(f\"Audit course: {action[1]}\")\n", + " else:\n", + " print(f\"Consult student {action[1].id} about courses: {', '.join(action[1].courses)}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "G_N8-asfnBEQ", + "outputId": "91390270-0c3f-43ac-a9ca-05278bb27c65" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Consult student 1 about courses: CS102, CS101\n", + "Consult student 3 about courses: CS104, CS103\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Explanation**\n", + "* Student class: Represents a student with an ID and the set of courses they have taken.\n", + "* select_courses_and_students function: Implements the greedy approach to select courses to audit and students to consult.\n", + "* The function iteratively picks the action (auditing a course or consulting a student) that maximizes the number of newly covered courses, considering the limit of k hours." + ], + "metadata": { + "id": "l8C48-UZnEmw" + } + }, + { + "cell_type": "markdown", + "source": [ + "## **Q9:**\n", + "\n", + "You're responsible for managing the food supply for your aquarium's diverse marine animals. You have $n_s$ sharks, $n_t$ turtles, $n_o$ octopuses, and $n_d$ dolphins. To feed them, you have four kinds of food: $t_s$ tons of squid, $t_f$ tons of fish, $t_a$ tons of algae, and $t_k$ tons of krill. Sharks eat only squid and fish, turtles consume algae and fish, octopuses eat fish and krill, and dolphins will eat squid, fish, and krill. Each animal requires one ton of food to stay nourished for a month.\n", + "\n", + "Devise an algorithm that determines whether you have enough food to sustain all the animals for a month. Your algorithm should convert this problem into a network flow issue. Demonstrate the accuracy of your algorithm. (You do not need to analyze its runtime.)\n", + "\n", + "(Note: To argue for the correctness of your solution, you need to establish that your algorithm returns “Yes” if and only if it is indeed possible to adequately feed all the animals.)\n", + "\n", + "### **Answer:**\n", + "\n", + "To solve the problem of determining whether the available food is sufficient to feed all the marine animals in the aquarium for a month, we can use the concept of network flow, specifically the Maximum Flow problem. The idea is to model the situation as a flow network and then find the maximum flow in this network. If the maximum flow equals the total demand of all animals, then it is possible to feed all animals; otherwise, it is not.\n", + "\n", + "#### **Step-by-Step Solution**\n", + "\n", + "1. Construct the Flow Network:\n", + "\n", + "* Create a directed graph $G=(V,E)$ where $V$ is the set of vertices and $E$ is the set of edges.\n", + "* Add a source vertex $s$ and a sink vertex $t$.\n", + "* For each type of animal ($ns$ sharks, $nt$ turtles, $no$ octopuses, and $nd$ dolphins), add a vertex. Let's call them $S$,$T$,$O$, and $D$ respectively.\n", + "* For each type of food ($ts$ tons of squid, $tf$ tons of fish, $ta$ tons of algae, $tk$ tons of krill), add a vertex. Let's call them $Sq$,$F$,$A$, and $K4 respectively.\n", + "* Add edges from the source $s$ to each food vertex with capacities equal to the amount of each food type available.\n", + "* Add edges from each animal vertex to the sink $t$ with capacities equal to the number of animals of that type.\n", + "* Add edges between food vertices and animal vertices according to the diet of each animal. For example, an edge from $Sq$ to $S$ and $D$, from $F$ to $S$,$T$,$O$, and $D$, and so on. These edges should have infinite capacity (or a very large number).\n", + "\n", + "2. Apply a Maximum Flow Algorithm:\n", + "\n", + "* Use an algorithm like the Ford-Fulkerson method or the Edmonds-Karp algorithm to find the maximum flow in the network.\n", + "3. Analyze the Result:\n", + "\n", + "* If the maximum flow equals the total demand of all animals (sum of $ns$,$nt$,$no$, and $nd$), then it is possible to feed all the animals.\n", + "* If the maximum flow is less than the total demand, then it is not possible to feed all the animals with the available food.\n", + "\n", + "#### **Pseudo-code**\n", + "\n", + "*** *Attention: it's pseudocode for better explanation. It's not runnable.***\n", + "\n", + "\n" + ], + "metadata": { + "id": "AoREBM0KnPyA" + } + }, + { + "cell_type": "code", + "source": [ + "function canFeedAllAnimals(ns, nt, no, nd, ts, tf, ta, tk):\n", + " G = createFlowNetwork(ns, nt, no, nd, ts, tf, ta, tk)\n", + " maxFlow = calculateMaxFlow(G, source, sink)\n", + " totalDemand = ns + nt + no + nd\n", + " return maxFlow == totalDemand" + ], + "metadata": { + "id": "HBcuPJT9oynw" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Correctness Argument**\n", + "* If the algorithm returns \"Yes\" (maxFlow == totalDemand): This means we can assign each ton of food to a specific animal without any shortage, satisfying all animals' dietary requirements.\n", + "* If the algorithm returns \"No\" (maxFlow < totalDemand): This indicates that even under the most optimal distribution, the available food is insufficient to meet the total demand, meaning not all animals can be fed.\n", + "\n", + "The construction of the network ensures that each edge and vertex correctly represents the constraints and demands of the problem. The maximum flow found in such a network directly corresponds to the best possible way to distribute food among the animals. Hence, if the max flow equals the total demand, we can feed all animals, proving the algorithm's correctness.\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "OgVGzgsAo05o" + } + }, + { + "cell_type": "markdown", + "source": [ + "To create a Python program that solves this problem using network flow, we can use a library like NetworkX, which provides functionalities for creating and manipulating complex networks. We'll also need a maximum flow algorithm, which is conveniently available in NetworkX.\n", + "\n" + ], + "metadata": { + "id": "_hvh_E94pJQC" + } + }, + { + "cell_type": "code", + "source": [ + "pip install networkx\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4we3N-irpUiA", + "outputId": "c8496e05-aa6e-465c-e0da-f60dea85b04b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (3.2.1)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import networkx as nx\n", + "\n", + "def create_flow_network(ns, nt, no, nd, ts, tf, ta, tk):\n", + " G = nx.DiGraph()\n", + "\n", + " # Add vertices for animals\n", + " G.add_node(\"Sharks\", demand=-ns)\n", + " G.add_node(\"Turtles\", demand=-nt)\n", + " G.add_node(\"Octopuses\", demand=-no)\n", + " G.add_node(\"Dolphins\", demand=-nd)\n", + "\n", + " # Add vertices for food\n", + " G.add_node(\"Squid\", demand=ts)\n", + " G.add_node(\"Fish\", demand=tf)\n", + " G.add_node(\"Algae\", demand=ta)\n", + " G.add_node(\"Krill\", demand=tk)\n", + "\n", + " # A large number to simulate 'infinite' capacity\n", + " large_number = ns + nt + no + nd + ts + tf + ta + tk\n", + "\n", + " # Add edges based on dietary needs\n", + " G.add_edge(\"Squid\", \"Sharks\", capacity=large_number)\n", + " G.add_edge(\"Squid\", \"Dolphins\", capacity=large_number)\n", + " G.add_edge(\"Fish\", \"Sharks\", capacity=large_number)\n", + " G.add_edge(\"Fish\", \"Turtles\", capacity=large_number)\n", + " G.add_edge(\"Fish\", \"Octopuses\", capacity=large_number)\n", + " G.add_edge(\"Fish\", \"Dolphins\", capacity=large_number)\n", + " G.add_edge(\"Algae\", \"Turtles\", capacity=large_number)\n", + " G.add_edge(\"Krill\", \"Octopuses\", capacity=large_number)\n", + " G.add_edge(\"Krill\", \"Dolphins\", capacity=large_number)\n", + "\n", + " return G\n", + "\n", + "def can_feed_all_animals(ns, nt, no, nd, ts, tf, ta, tk):\n", + " G = create_flow_network(ns, nt, no, nd, ts, tf, ta, tk)\n", + "\n", + " # Run the max flow algorithm\n", + " flow_value, flow_dict = nx.maximum_flow(G, 'Squid', 'Krill')\n", + "\n", + " # Check if the flow meets the total demand\n", + " total_demand = ns + nt + no + nd\n", + " return flow_value >= total_demand\n", + "\n", + "# Example usage\n", + "ns, nt, no, nd = 10, 5, 7, 8 # Number of each animal\n", + "ts, tf, ta, tk = 20, 30, 15, 25 # Tons of each food\n", + "\n", + "result = can_feed_all_animals(ns, nt, no, nd, ts, tf, ta, tk)\n", + "print(\"Can all animals be fed?\", result)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fU7KETa_p8mm", + "outputId": "88b1e33d-8b49-4629-af83-8f5a5a590603" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Can all animals be fed? False\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **How It Works:**\n", + "1. Creating the Flow Network\n", + "\n", + "The function create_flow_network constructs a directed graph (flow network) representing our problem.\n", + "\n", + "* Animal Nodes: Each animal type (sharks, turtles, octopuses, dolphins) is added as a node with a \"demand\" that is negative. This negative demand represents the amount of food required by each type of animal.\n", + "* Food Nodes: Each food type (squid, fish, algae, krill) is added as a node with a \"demand\" that is positive, representing the available quantity of that food.\n", + "* Edges: Edges are added between food nodes and animal nodes based on the diet of each animal type. The capacity of these edges is set to a large number, which effectively means there's no realistic limit to how much of a particular food can be fed to an animal type.\n", + "\n", + "2. Large Number as Capacity\n", + "\n", + "* We use a large number to represent what we initially wanted as 'infinite' capacity on the edges. This number (large_number) is calculated as the sum of all animals and all food quantities, ensuring it's sufficiently large to not constrain the flow in the network.\n", + "\n", + "3. The Maximum Flow Algorithm\n", + "\n", + "* The function can_feed_all_animals calculates the maximum flow in the network using the nx.maximum_flow function. This function finds the best way to distribute the available food (flow) to meet the demands of all animals.\n", + "* The algorithm computes both the total flow value (flow_value) and the flow distribution (flow_dict). However, for our purpose, we're only interested in the total flow value.\n", + "\n", + "4. Checking if All Animals Can Be Fed\n", + "\n", + "* After computing the maximum flow, the code checks if this flow value is greater than or equal to the total demand of all animals (total_demand).\n", + "* If the maximum flow value is at least equal to the total demand, it means that there's a way to distribute the available food such that all animals' food requirements are met.\n", + "* If it's less, it indicates that not all animals can be fed with the available food.\n", + "\n", + "5. Example Usage\n", + "* The code concludes with an example where the numbers of each type of animal and the quantities of each type of food are defined.\n", + "* The can_feed_all_animals function is then called with these numbers, and the result is printed, indicating whether or not it's possible to feed all the animals with the available food.\n", + "\n", + "This approach effectively turns our problem into a network flow problem and uses the maximum flow algorithm to find out if the available food resources are sufficient to meet the demands of all the animals in the aquarium." + ], + "metadata": { + "id": "jVvOPskTpbOc" + } + }, + { + "cell_type": "markdown", + "source": [ + "## **Reflection:**\n", + "\n", + "1. How ChatGPT or the tool you used assisted in this task?\n", + "\n", + "* Firstly, I delved deeper into the foundational knowledge and principles of the algorithms needed for this assignment through ChatGPT. I must admit that the NP-complete problem and Maximum Flow problem related knowledge is quite challenging, involving extensive theoretical knowledge and comprehensive derivations. It's essential to have a profound understanding and application of these concepts, especially the details. During this process, it was critical to ensure that the newly created algorithmic problems remained consistent with the original core of the existing algorithmic problems.\n", + "\n", + "* To overcome these challenges, I used ChatGPT to ensure that the new algorithmic questions were in line with the original algorithmic core. The process went something like this: 1. First, understand the problem statement and the general algorithm. 2. Conduct research with ChatGPT to identify which aspects of the algorithm the problem mainly examines. 3. Based on this, extensively browse similar problems online and in books to create new, original algorithmic questions. 4. Use ChatGPT again to check if the new problem aligns with the original problem's core essence.\n", + "\n", + "* During this time, the complexity of creating algorithmic solutions also significantly increased. I spent a considerable amount of time learning the algorithmic knowledge and theoretical foundations of NP-complete problem and Maximum Flow problem, focusing on the details of the problem, and translating them into executable code to solve specific algorithmic challenges. In this phase, ChatGPT became an invaluable ally. It simplified the path to intuitively understanding new knowledge and provided a good platform and possibilities for various experiments.\n", + "\n", + "2. Challenges you faced while ensuring the problem maintained the spirit of the example\n", + "\n", + "* The primary challenge I encountered in this assignment was gaining a deeper understanding of NP-completeness. Many problems were difficult for me to solve on the first attempt, even when I already knew the correct solutions. When it came to creating and solving new problems, there were times I found them challenging as well. This made me realize that my knowledge of these types of problems was insufficient, prompting me to further study them after class.\n", + "\n", + "* Additionally, I sometimes faced issues with code not running successfully. In such instances, my first approach was to attempt debugging myself, and if that failed, I would use ChatGPT for assistance. Often, these issues were related to boundary cases like array out-of-bounds errors, highlighting the need for me to strengthen my practical coding skills. However, after several attempts, these problems were successfully resolved, significantly improving my hands-on abilities.\n", + "\n", + "* Another challenge was beginning to write pseudocode by myself to abstract and template solutions for certain types of problems. This process was quite difficult, but using ChatGPT provided me with numerous learning examples, helping me learn how to write these by myself and verify the accuracy of my pseudocode. Overall, this assignment greatly enhanced my understanding and application of maximum flow problems and NP-complete problems.\n", + "\n", + "3. What you learned about problem design in the realm of algorithms\n", + "\n", + "* In this assignment, I deepened my understanding of NP-Completeness and Maximum Flow problems, gaining a more profound grasp of both their theoretical underpinnings and practical applications. This experience has highlighted the intricacies of problem design in the realm of algorithms.\n", + "\n", + "* Firstly, I learned that designing algorithmic problems requires a balance between theoretical concepts and their practical implementation. NP-Complete problems, for instance, illustrate the complexity of computational theory. Understanding their theoretical background is essential for designing problems that are not just challenging but also meaningful in the context of computational limits.\n", + "\n", + "* Secondly, working with Maximum Flow problems taught me about optimization and the efficient allocation of resources in a network. Designing problems in this area helped me appreciate the importance of visualizing data flow and capacity constraints, which is crucial for creating realistic and solvable algorithmic challenges.\n", + "\n", + "* Lastly, the experience underscored the importance of precision in problem design. Even minor oversights or inaccuracies can lead to fundamentally different problems, necessitating a meticulous approach to defining problem parameters and conditions.\n", + "\n", + "* Overall, this assignment enriched my perspective on algorithm design, emphasizing the importance of a strong theoretical foundation, practical applicability, and attention to detail in crafting meaningful and solvable algorithmic challenges." + ], + "metadata": { + "id": "My7536QMpvjL" + } + }, + { + "cell_type": "markdown", + "source": [ + "## **Reference:**\n", + "* https://www.britannica.com/science/NP-complete-problem\n", + "* https://ics.uci.edu/~eppstein/161/960312.html\n", + "* https://www.cs.cmu.edu/~ckingsf/bioinfo-lectures/netflow.pdf\n", + "* https://stackoverflow.com/questions/1857244/what-are-the-differences-between-np-np-complete-and-np-hard\n", + "* https://themanoftalent.medium.com/np-hard-problems-21852451488a\n", + "\n" + ], + "metadata": { + "id": "MMQIbkNWusSr" + } + }, + { + "cell_type": "markdown", + "source": [ + "## **License:**\n", + "Copyright <2023> < Yanyan Chen>\n", + "\n", + "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n", + "\n", + "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", + "\n", + "THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." + ], + "metadata": { + "id": "88MWs8ldwkA2" + } + } + ] +} \ No newline at end of file diff --git a/Submissions/002799697_Yanyan_Chen/002799697_Yanyan_Chen_Assignment4_README.md b/Submissions/002799697_Yanyan_Chen/002799697_Yanyan_Chen_Assignment4_README.md new file mode 100644 index 0000000..2ec98c8 --- /dev/null +++ b/Submissions/002799697_Yanyan_Chen/002799697_Yanyan_Chen_Assignment4_README.md @@ -0,0 +1,13 @@ +# 002799697_Yanyan_Chen_Assignment4 + +## Summary + +Name: Yanyan Chen + +NUID: 002799697 + +Date: 11/19/2023 + +* This assignment is mainly designed to focus on the knowledge points in worked assignment 4. +* Each question in the homework is given a detailed answer and idea analysis. Most of the answers include code or pseudo-code to better explain its principles. +* The Reflections, References, and License involved in this assignment are included in the file 002799697_Yanyan_Chen_Assignment4.ipynb