Merge pull request #683 from NVIDIA/release/25.12

Forward-merge release/25.12 into main

Author: GPUtester

docs/cuopt/source/cuopt-python/lp-milp/examples/simple_qp_example.py

@@ -0,0 +1,57 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Simple Quadratic Programming Example
+====================================
+
+.. note::
+   Quadratic Programming support is currently experimental and may change
+   in future releases.
+
+This example demonstrates how to formulate and solve a simple
+Quadratic Programming (QP) problem using the cuOpt Python API.
+
+Problem:
+    minimize    x^2 + y^2
+    subject to  x + y >= 1
+                x, y >= 0
+
+This is a convex QP that minimizes the squared distance from the origin
+while requiring the sum of x and y to be at least 1.
+"""
+
+from cuopt.linear_programming.problem import (
+    MINIMIZE,
+    Problem,
+)
+
+
+def main():
+    # Create a new optimization problem
+    prob = Problem("Simple QP")
+
+    # Add variables with non-negative bounds
+    x = prob.addVariable(lb=0, name="x")
+    y = prob.addVariable(lb=0, name="y")
+
+    # Add constraint: x + y >= 1
+    prob.addConstraint(x + y >= 1, name="sum_constraint")
+
+    # Set quadratic objective: minimize x^2 + y^2
+    # Using Variable * Variable to create quadratic terms
+    quad_obj = x * x + y * y
+    prob.setObjective(quad_obj, sense=MINIMIZE)
+
+    # Solve the problem
+    prob.solve()
+
+    # Print results
+    print(f"Optimal solution found in {prob.SolveTime:.2f} seconds")
+    print(f"x = {x.Value}")
+    print(f"y = {y.Value}")
+    print(f"Objective value = {prob.ObjValue}")
+
+
+if __name__ == "__main__":
+    main()

docs/cuopt/source/cuopt-python/lp-milp/lp-milp-api.rst

@@ -16,6 +16,13 @@ LP and MILP API Reference
    :members:
    :undoc-members:
 
+.. note::
+   Quadratic Programming support (QuadraticExpression, QuadraticTerm) is currently **experimental** and may change in future releases.
+
+.. autoclass:: cuopt.linear_programming.problem.QuadraticExpression
+   :members:
+   :undoc-members:
+
 .. autoclass:: cuopt.linear_programming.problem.Constraint
    :members:
    :undoc-members:

docs/cuopt/source/cuopt-python/lp-milp/lp-milp-examples.rst

@@ -27,6 +27,27 @@ The response is as follows:
     y = 0.0
     Objective value = 10.0
 
+Simple Quadratic Programming Example
+------------------------------------
+
+.. note::
+   Quadratic Programming support is currently **experimental** and may change in future releases.
+
+:download:`simple_qp_example.py <examples/simple_qp_example.py>`
+
+.. literalinclude:: examples/simple_qp_example.py
+   :language: python
+   :linenos:
+
+The response is as follows:
+
+.. code-block:: text
+
+    Optimal solution found in 0.01 seconds
+    x = 0.5
+    y = 0.5
+    Objective value = 0.5
+
 Mixed Integer Linear Programming Example
 ----------------------------------------
 

docs/cuopt/source/lp-features.rst

@@ -23,6 +23,36 @@ The LP solver can be accessed in the following ways:
 
 Each option provide the same powerful linear optimization capabilities while offering flexibility in deployment and integration.
 
+Quadratic Programming (QP)
+--------------------------
+
+.. note::
+   Quadratic Programming support is currently **experimental** and may change in future releases.
+
+cuOpt supports Quadratic Programming problems with quadratic objectives of the form:
+
+.. code-block:: text
+
+    minimize (or maximize)    x'Qx + c'x
+    subject to                Ax {<=, =, >=} b
+                              lb <= x <= ub
+
+where Q is a symmetric positive semi-definite matrix for minimization problems.
+
+In the Python API, quadratic objectives can be constructed using the ``QuadraticExpression`` class or by multiplying variables directly:
+
+.. code-block:: python
+
+    from cuopt.linear_programming.problem import Problem, MINIMIZE
+
+    prob = Problem("QP Example")
+    x = prob.addVariable(lb=0, name="x")
+    y = prob.addVariable(lb=0, name="y")
+
+    # Create quadratic objective: x^2 + 2*x*y + y^2
+    quad_obj = x * x + 2 * (x * y) + y * y
+    prob.setObjective(quad_obj, sense=MINIMIZE)
+
 Variable Bounds
 ---------------