Add docs and example tests to barrier #449

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Merged

rapids-bot merged 51 commits into NVIDIA:branch-25.10 from rgsl888prabhu:barrier_doc_and_tests

Oct 10, 2025

ci/test_wheel_cuopt.sh

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+Diff line change
@@ Expand Up / @@ -66,11 +66,14 @@ cd - @@
     RAPIDS_DATASET_ROOT_DIR="$(realpath datasets)"
     export RAPIDS_DATASET_ROOT_DIR
-    # Please enable this once ISSUE https://github.com/NVIDIA/cuopt/issues/94 is fixed
     # Run CLI tests
     timeout 10m bash ./python/libcuopt/libcuopt/tests/test_cli.sh
     # Run Python tests
+    # Due to race condition in certain cases UCX might not be able to cleanup properly, so we set the number of threads to 1
+    export OMP_NUM_THREADS=1
     RAPIDS_DATASET_ROOT_DIR=./datasets timeout 30m python -m pytest --verbose --capture=no ./python/cuopt/cuopt/tests/
     # run jump tests and cvxpy integration tests for only nightly builds
@@ Expand Down @@

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

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@@ Expand Up @@
     .. doxygenfunction:: cuOptGetIntSize
     .. doxygenfunction:: cuOptGetFloatSize
+    Version Information
+    -------------------
+    You may use the following function to get the version of the cuOpt library
+    .. doxygenfunction:: cuOptGetVersion
     Status Codes
     ------------
@@ Expand All @@
     .. doxygendefine:: CUOPT_INVALID_ARGUMENT
     .. doxygendefine:: CUOPT_MPS_FILE_ERROR
     .. doxygendefine:: CUOPT_MPS_PARSE_ERROR
+    .. doxygendefine:: CUOPT_VALIDATION_ERROR
+    .. doxygendefine:: CUOPT_OUT_OF_MEMORY
+    .. doxygendefine:: CUOPT_RUNTIME_ERROR
     Optimization Problem
     --------------------
@@ Expand Down Expand Up @@
     .. doxygendefine:: CUOPT_MIP_ABSOLUTE_TOLERANCE
     .. doxygendefine:: CUOPT_MIP_RELATIVE_TOLERANCE
     .. doxygendefine:: CUOPT_MIP_INTEGRALITY_TOLERANCE
+    .. doxygendefine:: CUOPT_MIP_ABSOLUTE_GAP
+    .. doxygendefine:: CUOPT_MIP_RELATIVE_GAP
     .. doxygendefine:: CUOPT_MIP_SCALING
     .. doxygendefine:: CUOPT_MIP_HEURISTICS_ONLY
+    .. doxygendefine:: CUOPT_MIP_PRESOLVE
     .. doxygendefine:: CUOPT_PRESOLVE
+    .. doxygendefine:: CUOPT_LOG_TO_CONSOLE
+    .. doxygendefine:: CUOPT_CROSSOVER
+    .. doxygendefine:: CUOPT_FOLDING
+    .. doxygendefine:: CUOPT_AUGMENTED
+    .. doxygendefine:: CUOPT_DUALIZE
+    .. doxygendefine:: CUOPT_ORDERING
+    .. doxygendefine:: CUOPT_ELIMINATE_DENSE_COLUMNS
+    .. doxygendefine:: CUOPT_CUDSS_DETERMINISTIC
+    .. doxygendefine:: CUOPT_BARRIER_DUAL_INITIAL_POINT
+    .. doxygendefine:: CUOPT_DUAL_POSTSOLVE
     .. doxygendefine:: CUOPT_SOLUTION_FILE
     .. doxygendefine:: CUOPT_NUM_CPU_THREADS
     .. doxygendefine:: CUOPT_USER_PROBLEM_FILE
@@ Expand Down Expand Up @@
     .. doxygendefine:: CUOPT_METHOD_CONCURRENT
     .. doxygendefine:: CUOPT_METHOD_PDLP
     .. doxygendefine:: CUOPT_METHOD_DUAL_SIMPLEX
+    .. doxygendefine:: CUOPT_METHOD_BARRIER
     Solving an LP or MIP
@@ Expand All / @@ -206,12 +230,15 @@ The output of a solve is a `cuOptSolution` object. @@
     The following functions may be used to access information from a `cuOptSolution`
     .. doxygenfunction:: cuOptGetTerminationStatus
+    .. doxygenfunction:: cuOptGetErrorStatus
+    .. doxygenfunction:: cuOptGetErrorString
     .. doxygenfunction:: cuOptGetPrimalSolution
     .. doxygenfunction:: cuOptGetObjectiveValue
     .. doxygenfunction:: cuOptGetSolveTime
     .. doxygenfunction:: cuOptGetMIPGap
     .. doxygenfunction:: cuOptGetSolutionBound
     .. doxygenfunction:: cuOptGetDualSolution
+    .. doxygenfunction:: cuOptGetDualObjectiveValue
     .. doxygenfunction:: cuOptGetReducedCosts
     When you are finished with a `cuOptSolution` object you should destory it with
@@ Expand Down @@

docs/cuopt/source/cuopt-python/routing/routing-example.ipynb

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docs/cuopt/source/introduction.rst

            
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    @@ -66,9 +66,15 @@ This is a linear program.
  
    How cuOpt Solves the Linear Programming Problem

    ------------------------------------------------

    cuOpt includes an LP solver based on `PDLP <https://arxiv.org/abs/2106.04756>`__, a new First-Order Method (FOM) used to solve large-scale LPs. This solver implements gradient descent, enhanced by heuristics, and performing massively parallel operations efficiently by leveraging the latest NVIDIA GPUs.

    cuOpt includes three LP solving methods:

    In addition to PDLP, cuOpt includes a dual simplex solver that runs on the CPU. Both algorithms can be run concurrently on the GPU and CPU.

    * **PDLP**: Based on `PDLP <https://arxiv.org/abs/2106.04756>`__, a First-Order Method (FOM) for solving large-scale LPs. This solver implements primal-dual hybrid gradient enhanced by heuristics. Sparse matrix-vector products are perfomed efficiently on NVIDIA GPUs.

    * **Barrier (Interior-Point)**: A primal-dual interior-point method that uses GPU-accelerated sparse Cholesky and LDLT solves via cuDSS, and sparse matrix operations via cuSparse.

    * **Dual Simplex**: A CPU-based dual simplex solver for small to medium-sized problems.

    All three algorithms can be run concurrently on both GPU and CPU, with the fastest solution returned automatically.

    Mixed Integer Linear Programming (MILP)

    =========================================

    @@ -121,6 +127,7 @@ cuOpt supports the following APIs:
  
       - `AMPL <https://www.ampl.com/>`_

       - `GAMS <https://www.gams.com/>`_

       - `PuLP <https://pypi.org/project/PuLP/>`_

       - `JuMP <https://github.com/jump-dev/cuOpt.jl>`_

    ==================================

docs/cuopt/source/lp-features.rst

            
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    @@ -13,6 +13,7 @@ The LP solver can be accessed in the following ways:
  
       -  AMPL

       -  GAMS

       -  PuLP

       -  JuMP

    - **C API**: A native C API that provides direct low-level access to cuOpt's LP capabilities, enabling integration into any application or system that can interface with C.

    @@ -65,17 +66,19 @@ Users can control how the solver will operate by specifying the PDLP solver mode
  
    Method

    ------

    **Concurrent**: The default method for solving linear programs. When concurrent is selected, cuOpt runs two algorithms at the same time: PDLP on the GPU and dual simplex on the CPU. A solution is returned from the algorithm that finishes first.

    **Concurrent**: The default method for solving linear programs. When concurrent is selected, cuOpt runs three algorithms in parallel: PDLP on the GPU, barrier (interior-point) on the GPU, and dual simplex on the CPU. A solution is returned from the algorithm that finishes first.

    **PDLP**: Primal-Dual Hybrid Gradient for Linear Program is an algorithm for solving large-scale linear programming problems on the GPU. PDLP does not attempt to any matrix factorizations during the course of the solve. Select this method if your LP is so large that factorization will not fit into memory. By default PDLP solves to low relative tolerance and the solutions it returns do not lie at a vertex of the feasible region. Enable crossover to obtain a highly accurate basic solution from a PDLP solution.

    **PDLP**: Primal-Dual Hybrid Gradient for Linear Program is an algorithm for solving large-scale linear programming problems on the GPU. PDLP does not attempt any matrix factorizations during the course of the solve. Select this method if your LP is so large that factorization will not fit into memory. By default PDLP solves to low relative tolerance and the solutions it returns do not lie at a vertex of the feasible region. Enable crossover to obtain a highly accurate basic solution from a PDLP solution.

    **Barrier**: The barrier method (also known as interior-point method) solves linear programs using a primal-dual predictor-corrector algorithm. This method uses GPU-accelerated sparse Cholesky and sparse LDLT solves via cuDSS, and GPU-accelerated sparse matrix-vector and matrix-matrix operations via cuSparse. Barrier is particularly effective for large-scale problems and can automatically apply techniques like folding, dualization, and dense column elimination to improve performance. This method solves the linear systems at each iteration using the augmented system or the normal equations (ADAT). Enable crossover to obtain a highly accurate basic solution from a barrier solution.

    **Dual Simplex**: Dual simplex is the simplex method applied to the dual of the linear program. Dual simplex requires the basis factorization of linear program fit into memory. Select this method if your LP is small to medium sized, or if you require a high-quality basic solution.

    Crossover

    ---------

    Crossover allows you to obtain a high-quality basic solution from the results of a PDLP solve. More details can be found :ref:`here <crossover>`.

    Crossover allows you to obtain a high-quality basic solution from the results of a PDLP or barrier solve. When enabled, crossover converts these solutions to a vertex solution (basic solution) with high accuracy. More details can be found :ref:`here <crossover>`.

    Presolve

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