update docs

Author: andrewrosemberg

…ss01/background_materials/basics_math.jl …ss01/background_materials/math_basics.jlclass01/background_materials/basics_math.jl renamed to class01/background_materials/math_basics.jl class01/background_materials/basics_math.jl renamed to class01/background_materials/math_basics.jl

@@ -7,6 +7,7 @@ using InteractiveUtils
 # ╔═╡ 9fffca86-aec9-4c26-bced-3edaa1af9a22
 begin
 using Pkg; Pkg.activate("..")
+Pkg.instantiate()
 end
 
 # ╔═╡ 00cc1e5e-f10e-4559-b9d9-8aba44eb493e

…round_materials/optimization_homework.jl …kground_materials/optimization_basics.jlclass01/background_materials/optimization_homework.jl renamed to class01/background_materials/optimization_basics.jl class01/background_materials/optimization_homework.jl renamed to class01/background_materials/optimization_basics.jl

@@ -7,6 +7,7 @@ using InteractiveUtils
 # ╔═╡ 881eed45-e7f0-4785-bde8-530e378d7050
 begin
 using Pkg; Pkg.activate("..")
+Pkg.instantiate()
 end
 
 # ╔═╡ 9f5675a3-07df-4fb1-b683-4c5fd2a85002

class01/Optimization.jl …und_materials/optimization_motivation.jlclass01/Optimization.jl renamed to class01/background_materials/optimization_motivation.jl class01/Optimization.jl renamed to class01/background_materials/optimization_motivation.jl

@@ -7,7 +7,7 @@ using InteractiveUtils
 # ╔═╡ f0c826c7-b2e3-4dbf-b29d-37623aa4d7c6
 begin
 	import Pkg
-	Pkg.activate(".")
+	Pkg.activate("..")
 	Pkg.status()
 end
 
@@ -110,8 +110,8 @@ begin
 	for (ri,y) in enumerate(h_cs), (ci,x) in enumerate(v_cs)
 		global vid
 	    # skip the outer frame
-		if !(ri == n_rows -5 && ci == 4)
-		    if ri <= 27 || ri >= n_rows - 3 || ci <= 6 || ci >= n_cols - 3
+		if !(ri == n_rows -5 && ci ==3)
+		    if ri <= 25 || ri >= n_rows - 4 || ci <= 8 || ci >= n_cols - 2
 		        continue                       
 		    end
 		end
@@ -129,7 +129,12 @@ begin
 	end
 	@info "Vertices = $(nv(g))  |  Edges = $(ne(g))"
 
-	add_edge!(g, 211, 212)
+	# add entrance
+	add_edge!(g, 173, 174)
+
+	# TODO: Automate ensure connectivity
+	add_edge!(g, 104, 83)
+	add_edge!(g, 104, 105)
 
 	overlay_img = overlay(img, h_cs, v_cs, verts) 
 end
@@ -167,7 +172,7 @@ begin
 
 	    sort(sel)
 	end
-	function overlay_items(img_rgb, hcs, vcs, verts, list, start_node=211, end_node=102; dot_half = 3, fade::Float64   = 0.40, gradc=false
+	function overlay_items(img_rgb, hcs, vcs, verts, list, start_node=173, end_node=46; dot_half = 3, fade::Float64   = 0.40, gradc=false
 	)
 	    grey = RGB.(Gray.(img_rgb))
 	    ol   = map(c -> RGB((1 - fade) * c.r + fade,
@@ -253,15 +258,15 @@ Date: 28 of July, 2025
 # ╔═╡ 01c44cc2-68d5-11f0-2860-05c9ffbde13a
 md"# Decisions Decisions: A Path to optimality
 
-Once upon a time, a boy named **Pedro Paulo** 🤵 loved **Costco** supermarket 🛒. However, everytime there, he spendt more time ⏱️ and money 💸 than needed.
+Once upon a time, a boy named **Pedro Paulo** 🤵 loved shopping at his local supermarket 🛒. However, everytime there, he spendt more time ⏱️ and money 💸 than needed.
 
 Let's help Pedro out!
 "
 
 # ╔═╡ 8d0fe751-aeb1-4ad1-a076-4c7bcd863a55
 md"## Problem Setting
 
-After some investigation, we got a hold of a real Costco layout! 🎉🎉 🗺️ 🎉🎉
+After some investigation, we got a hold of the market layout! 🎉🎉 🗺️ 🎉🎉
 "
 
 # ╔═╡ 9eb11624-17db-438e-86e4-77b313da268b
@@ -360,7 +365,7 @@ begin
 end
 
 # ╔═╡ 8705f186-8af4-4f6b-bb53-c3fa46d0b8ba
-function greedy_tour(list, dist; start_node=211, end_node=102)
+function greedy_tour(list, dist; start_node=173, end_node=46)
     tour = [start_node]
     remaining = Set([list[1:end]; end_node])
     while !isempty(remaining)
@@ -430,7 +435,7 @@ question_box(md"### How to model it as an integer programing problem?")
 
 # ╔═╡ acb9d0fd-c024-44b7-b549-78875068050f
 begin
-	nverts=length(verts); nitems=length(plist);start_node=211;end_node=102;
+	nverts=length(verts); nitems=length(plist);start_node=173;end_node=46;
 	md"""
 	#### What we have: 
 	
@@ -476,41 +481,16 @@ begin
 	#  BASIC SETS
 	_V     = vertices(g)
 	_A     = [(u,v) for e in edges(g) for (u,v) in ((src(e),dst(e)), (dst(e),src(e)))]
-	_s, _e = 211, 102 						 # start, end
+	_s, _e = 173, 46 						 # start, end
 	_items = setdiff(Set(plist), [_s,_e])
 	K      = length(_items) + 1              # units of flow we must deliver
 
 	# MODEL
 	model = Model(HiGHS.Optimizer)
 
-	@variable(model, x[_A], Bin)
-	@variable(model, 0 ≤ f[_A] ≤ K)
-	
-	out(v)  = sum(x[(v,w)]   for w in _V if (v,w) in _A)
-	inn(v)  = sum(x[(u,v)]   for u in _V if (u,v) in _A)
-	fout(v) = sum(f[(v,w)]   for w in _V if (v,w) in _A)
-	finn(v) = sum(f[(u,v)]   for u in _V if (u,v) in _A)
-	
-	## degree / balance
-	@constraint(model,  out(_s) - inn(_s) == 1)        # Costco Entrance
-	@constraint(model,  inn(_e) - out(_e) == 1)        # Payment Exit
-	@constraint(model, [i in _items],  inn(i) ≥ 1)     # Pick up items
-	@constraint(model, [i in _items],  out(i) ≥ 1)
-	@constraint(model, [v in setdiff(_V, [_s,_e])], inn(v) == out(v))
-	
-	## multi-unit single-commodity flow
-	@constraint(model, [a in _A], f[a] ≤ K * x[a])     # deactivate on unused arcs
-	
-	@constraint(model, fout(_s) - finn(_s) ==  K)      # source injects K units
-	@constraint(model, finn(_e) - fout(_e) ==  1)      # _e ‘consumes’ one unit
-	@constraint(model, [i in _items],  finn(i) - fout(i) == 1)   # each item consumes 1
-	@constraint(model, [v in setdiff(_V, [_s,_e] ∪ _items)],
-	                      fout(v) == finn(v))          # pure transshipment elsewhere
-	
-	## objective
-	@objective(model, Min, sum(D[u,v] * x[(u,v)] for (u,v) in _A))
-	
-	optimize!(model)
+	# Write your Model Here
+
+	itinerary_answer = missing # replace missing with the optimal itinerary
 end
 
 # ╔═╡ 8e507678-5d40-4a44-9aac-5701cc27f8ad
@@ -534,59 +514,42 @@ A[i,j] =
 	 
 ")
 
-# ╔═╡ f7dc2333-d85f-44d7-bd5e-2f497a23f32b
-function euler!(adj, start)
-	st=[start]; path=Int[]
-	while !isempty(st)
-		u = st[end]
-		if isempty(get(adj,u,Int[]))
-			push!(path, pop!(st))
-		else
-			push!(st, pop!(adj[u]))           # consume arc once
-		end
-	end
-	reverse(path)                             # ends at _e by construction
-end
-
-# ╔═╡ 7db1a83c-0100-4b6b-a97b-a7d59520dc22
-begin
-	adj = Dict{Int,Vector{Int}}()
-	for (u,v) in _A
-	    if value(x[(u,v)]) > 0.5
-	        push!(get!(adj,u,Int[]), v)
-	    end
-	end
-	itinerary_answer = euler!(deepcopy(adj), _s)
-	@assert itinerary_answer[1] == _s && itinerary_answer[end] == _e
-	itinerary_answer
-end
-
-# ╔═╡ 1c527fc1-f4fd-4a59-8f0b-63413b1bea56
-# itinerary_answer = missing # replace with your answer
-
 # ╔═╡ f5faca5f-abc3-49f1-add5-385de0ddb5b7
 begin
 	if ismissing(itinerary_answer)
 		still_missing()
 	else
 		length_walk = itinerary_distance(itinerary_answer, D)
-		if length_walk <= 312
+		if length_walk <= 312 && itinerary_answer[1] == _s && itinerary_answer[end] == _e
 			correct(md" $(round(length_walk)) meters. You have found the optimal path!")
-		elseif length_walk <= total_length
+		elseif length_walk <= total_length && itinerary_answer[1] == _s && itinerary_answer[end] == _e
 			almost(md" $(round(length_walk)) meters. Nice you have beaten the greedy algorithm! But there is still room for improvement.")
 		else
 			keep_working(md" $(round(length_walk)) meters. You should at least beat the greedy algorithm. I thrust in you!")
 		end
 	end
 end
 
+# ╔═╡ f7dc2333-d85f-44d7-bd5e-2f497a23f32b
+function euler!(adj, start)
+	st=[start]; path=Int[]
+	while !isempty(st)
+		u = st[end]
+		if isempty(get(adj,u,Int[]))
+			push!(path, pop!(st))
+		else
+			push!(st, pop!(adj[u]))           # consume arc once
+		end
+	end
+	reverse(path)                             # ends at _e by construction
+end
+
 # ╔═╡ 5de8429f-349d-4441-90b4-6f7caaf6b5e4
 if !ismissing(itinerary_answer)
 	overlay_items(img,  h_cs, v_cs, verts, full_itinerary; gradc=true)
-end
-
-# ╔═╡ e7cc612d-fc6a-4976-89b2-821064dc612c
-still_missing()
+else
+	still_missing()
+end	
 
 # ╔═╡ e04cfe19-5827-4bf2-8183-a13bad579497
 md"## Now Let's get a bit crazy!
@@ -620,19 +583,7 @@ begin
 end
 
 # ╔═╡ 6ab03a08-6cda-4f35-bd80-e0ace02b86b5
-Foldable(md"#### Can we know the position of Pedro and his cart at any given moment?", md"""
-
-```math
-x[t+1] = x[t] + \dot{x}[t] * \Delta_t
-```
-```math
-\dot{x}[t] = \dot{x}[t-1] + \ddot{x}
-```
-
-""")
-
-# ╔═╡ e4baeff0-f380-4269-9818-9fc43c1f802d
-
+question_box(md"#### Can we know the position of Pedro and his cart at any given moment?")
 
 # ╔═╡ f31d8852-ca07-46c9-bbea-5dd8f476c25c
 	md"## References"
@@ -651,9 +602,6 @@ begin
 )
 end
 
-# ╔═╡ 2e57bcd7-af6a-4813-8a2a-f7a71752a2de
-
-
 # ╔═╡ Cell order:
 # ╟─f0c826c7-b2e3-4dbf-b29d-37623aa4d7c6
 # ╟─054eb7cf-cb60-41a0-9a87-215e36dcf53d
@@ -684,20 +632,15 @@ end
 # ╟─083ddea0-f1db-46ef-b82c-5e10499bfb9d
 # ╠═7c889414-b9c4-477d-8e57-79ee1518dc8c
 # ╟─8e507678-5d40-4a44-9aac-5701cc27f8ad
-# ╠═f7dc2333-d85f-44d7-bd5e-2f497a23f32b
-# ╠═7db1a83c-0100-4b6b-a97b-a7d59520dc22
-# ╠═1c527fc1-f4fd-4a59-8f0b-63413b1bea56
 # ╟─f5faca5f-abc3-49f1-add5-385de0ddb5b7
+# ╟─f7dc2333-d85f-44d7-bd5e-2f497a23f32b
 # ╟─5de8429f-349d-4441-90b4-6f7caaf6b5e4
-# ╠═e7cc612d-fc6a-4976-89b2-821064dc612c
 # ╟─e04cfe19-5827-4bf2-8183-a13bad579497
 # ╟─263969f2-5e0f-4d7b-9b42-eb033861e4e9
 # ╟─bf94beb1-f5c9-45b0-a864-94f773a3c198
 # ╟─85f6ac91-bcc7-44de-948c-5a631a82c846
 # ╠═bcaf8412-964e-4d79-8db8-d69754fe4b83
 # ╠═c0cdf191-5350-4738-882f-8ded20168dbb
-# ╠═6ab03a08-6cda-4f35-bd80-e0ace02b86b5
-# ╠═e4baeff0-f380-4269-9818-9fc43c1f802d
+# ╟─6ab03a08-6cda-4f35-bd80-e0ace02b86b5
 # ╟─f31d8852-ca07-46c9-bbea-5dd8f476c25c
 # ╟─178c6168-b515-4220-b37f-2c31b34e045c
-# ╠═2e57bcd7-af6a-4813-8a2a-f7a71752a2de

class01/class01.md

@@ -64,12 +64,19 @@ julia> Pluto.run()
 
 #### Step 5: Opening an existing notebook file
 
-To run a local notebook file that you have not opened before, then you need to enter its full path (e.g. `path/to/basics_math.jl`) into the blue box in the main menu.
+To run a local notebook file that you have not opened before, then you need to enter its full path (e.g. `path/to/math_basics.jl`) into the blue box in the main menu.
 
 ### **Linear Algebra**: 
-We have prepared a basic (Pluto) [Linear Algebra Primer](https://learningtooptimize.github.io/LearningToControlClass/dev/class01/background_materials/basics_math.html) to help you brush up on essential concepts. This primer covers key topics such as matrix operations, eigenvalues, and eigenvectors besides other fundamental calculus concepts. It is recommended to review this primer before the first class.
+We have prepared a basic (Pluto) [Linear Algebra Primer](https://learningtooptimize.github.io/LearningToControlClass/dev/class01/background_materials/math_basics.html) to help you brush up on essential concepts. This primer covers key topics such as matrix operations, eigenvalues, and eigenvectors besides other fundamental calculus concepts. It is recommended to review this primer before the first class.
 
 ### **Optimization**:
 We will use JuMP for some optimization tasks. If you are new to JuMP, please review the [JuMP Tutorial](https://jump.dev/JuMP.jl/stable/tutorials/getting_started/getting_started_with_JuMP/) to familiarize yourself with its syntax and capabilities.
 
-Test your knowledge with this (Pluto) [Modeling Exercise](https://learningtooptimize.github.io/LearningToControlClass/dev/class01/background_materials/optimization_homework.html).
+Test your knowledge with this (Pluto) [Modeling Exercise](https://learningtooptimize.github.io/LearningToControlClass/dev/class01/background_materials/optimization_basics.html).
+
+*Final 🧠*: The (Pluto) [Motivational Exercise](https://learningtooptimize.github.io/LearningToControlClass/dev/class01/background_materials/optimization_motivation.html) will test what you have learned and motivate what we will research in the course.
+
+## In-Class Material
+
+Besides the administrative topics, we will cover the structure of the problem we are trying to solve and start with the basics of how to model it.
+The main (Pluto) [Class 01 Notebook](https://learningtooptimize.github.io/LearningToControlClass/dev/class01/class01_intro.html) contains the in-class material.

class01/Dynamics.jl class01/class01_intro.jlclass01/Dynamics.jl renamed to class01/class01_intro.jl

@@ -7,7 +7,10 @@ repo_dir = dirname(@__DIR__)
 build_dir = joinpath(repo_dir, "docs", "build")
 
 plutos = [
-    joinpath(repo_dir, "class01", "background_materials", "basics_math.jl"),
+    joinpath(repo_dir, "class01", "background_materials", "math_basics.jl"),
+    joinpath(repo_dir, "class01", "background_materials", "optimization_basics.jl"),
+    joinpath(repo_dir, "class01", "background_materials", "optimization_motivation.jl"),
+    joinpath(repo_dir, "class01", "class01_intro.jl"),
 ]
 
 if !isdir(build_dir)
@@ -30,7 +33,9 @@ makedocs(
     ),
     pages  = [
         "Home"   => "index.md",
-        "Class 1" => ["class01/class01.md"],
+        "Class 1" => ["class01/class01.md",
+            "class01/background_materials/git_adventure_guide.md",
+        ],
     ],
 )