Update LP/QP/MILP docs to cover 26.06 API additions

docs/cuopt/source/lp-qp-features.rst → docs/cuopt/source/continuous-features.rst

@@ -1,26 +1,45 @@
-==================
-LP/QP Features
-==================
+========================
+LP/QP/SOCP Features
+========================
 
 Availability
 -------------
 
-The Linear Programming (LP) and Quadratic Programming (QP) solvers can be accessed in the following ways:
+The Linear Programming (LP), Quadratic Programming (QP), and Second-Order Cone Programming (SOCP) solvers can be accessed in the following ways:
 
-- **Third-Party Modeling Languages**: cuOpt's LP solver can be called directly from the following third-party modeling languages. This allows you to leverage GPU acceleration while maintaining your existing optimization workflow in these modeling languages.
+- **Third-Party Modeling Languages**: cuOpt's LP/QP solver can be called directly from the following third-party modeling languages. This allows you to leverage GPU acceleration while maintaining your existing optimization workflow in these modeling languages.
 
   Supported modeling languages:
-   -  AMPL
-   -  GAMS
-   -  PuLP
-   -  JuMP
+
+  .. list-table::
+     :header-rows: 1
+     :widths: 30 15 15
+
+     * - Language
+       - LP
+       - QP
+     * - AMPL
+       - ✓
+       -
+     * - CVXPY
+       - ✓
+       - ✓
+     * - GAMS
+       - ✓
+       - ✓
+     * - JuMP
+       - ✓
+       - ✓
+     * - PuLP
+       - ✓
+       -
 
 .. note::
-   The QP solver is not currently supported in third-party modeling languages.
+   SOCP is not currently supported in third-party modeling languages.
 
-- **C API**: A native C API that provides direct low-level access to cuOpt's LP/QP capabilities, enabling integration into any application or system that can interface with C.
+- **C API**: A native C API that provides direct low-level access to cuOpt's LP/QP/SOCP capabilities, enabling integration into any application or system that can interface with C.
 
-- **Python SDK**: A Python package that provides direct access to cuOpt's LP/QP capabilities through a simple, intuitive API. This allows for seamless integration into Python applications and workflows. For more information, see :doc:`cuopt-python/quick-start`.
+- **Python SDK**: A Python package that provides direct access to cuOpt's LP/QP/SOCP capabilities through a simple, intuitive API. This allows for seamless integration into Python applications and workflows. For more information, see :doc:`cuopt-python/quick-start`.
 
 - **As a Self-Hosted Service**: cuOpt's LP/QP solver can be deployed as a self-hosted service in your own infrastructure, enabling you to maintain full control while integrating it into your existing systems.
 
@@ -73,6 +92,48 @@ where Q is a symmetric positive semidefinite matrix. Please note that the Q matr
 See :ref:`simple-qp-example-python` for an example of how to create a QP problem with the Python Modeling API.
 See :ref:`simple-qp-example-c` for an example of how to create a QP problem with the C API.
 
+Second-Order Cone Programming (Beta)
+--------------------------------------
+
+.. note:: SOCP support is **beta** in this release.
+
+cuOpt supports Second-Order Cone Programming (SOCP) problems of the form:
+
+.. code-block:: text
+
+    minimize        c^T*x
+    subject to      A*x {<=, =, >=} b
+                    ||u_i||_2 <= t_i   (second-order cone constraints)
+                    lb <= x <= ub
+
+Because cuOpt does not accept cone constraints directly, SOC constraints are specified through the quadratic constraint API. Each SOC constraint ``||u||_2 <= t`` is expressed as the equivalent quadratic inequality ``u^T*u - t^2 <= 0``. cuOpt detects the SOC structure and converts to cone form internally before solving with the barrier method.
+
+.. note::
+   Only SOC-structured quadratic inequalities are supported. General quadratic constraints
+   (arbitrary quadratic forms) are not supported.
+
+When any quadratic constraint is present, cuOpt automatically selects the barrier method and disables presolve optimizations that apply only to linear problems.
+
+**Constraints:**
+
+- Only ``<=`` and ``>=`` sense is supported. Equality quadratic constraints are not supported.
+- The quadratic constraint must have valid second-order cone structure.
+
+**Python example** — expressing ``||[x, y]||_2 <= z`` as a quadratic inequality:
+
+.. code-block:: python
+
+    x = problem.addVariable("x", lb=0)
+    y = problem.addVariable("y", lb=0)
+    z = problem.addVariable("z", lb=0)
+
+    # SOC constraint ||[x,y]||_2 <= z expressed as x^2 + y^2 - z^2 <= 0
+    problem.addConstraint(x*x + y*y - z*z <= 0, name="soc")
+
+**C API:** Use :c:func:`cuOptAddQuadraticConstraint` to add SOC constraints expressed as quadratic inequalities. cuOpt detects the SOC structure and converts to cone form internally.
+
+.. note:: SOCP problems always use the barrier solver regardless of the ``CUOPT_METHOD`` setting.
+
 Warm Start
 -----------
 
@@ -117,7 +178,7 @@ Crossover allows you to obtain a high-quality basic solution from the results of
 Presolve
 --------
 
-Presolve procedure is applied to the problem before the solver is called. It can be used to reduce the problem size and improve solve time. cuOpt supports presolve reductions using PSLP or Papilo for linear programming (LP) problems, and Papilo for mixed-integer programming (MIP) problems. For MIP problems, Papilo presolve is always enabled by default. For LP problems, PSLP presolve is always enabled by default. Users can manually select to disable presolve by setting this parameter to 0, enable Papilo presolve by setting this parameter to 1, or enable PSLP presolve by setting this parameter to 2.
+Presolve procedure is applied to the problem before the solver is called. It can be used to reduce the problem size and improve solve time. cuOpt supports presolve reductions using PSLP or Papilo for linear programming (LP) problems. For LP problems, PSLP presolve is always enabled by default. Users can manually select to disable presolve by setting this parameter to 0, enable Papilo presolve by setting this parameter to 1, or enable PSLP presolve by setting this parameter to 2.
 Furthermore, for LP problems with Papilo presolver, when the dual solution is not needed, additional presolve procedures can be applied to further improve solve times. This is achieved by turning off dual postsolve with the ``CUOPT_DUAL_POSTSOLVE`` setting.
 
 

docs/cuopt/source/lp-qp-milp-settings.rst → docs/cuopt/source/continuous-settings.rst

@@ -1,16 +1,15 @@
-=================================
-LP, QP, and MILP Settings
-=================================
+================================================
+Continuous Optimization Settings (LP, QP, SOCP)
+================================================
 
 
-This page describes the parameter settings available for cuOpt's LP, QP, and MILP solvers. These parameters are set as :ref:`parameter constants <parameter-constants>` in case of C API and in case of Server Thin client as raw strings.
-Please refer to examples in :doc:`C </cuopt-c/lp-qp-milp/index>` and :doc:`Server Thin client </cuopt-server/index>` for more details.
+This page describes the parameter settings available for cuOpt's LP, QP, and SOCP solvers. These parameters are set as :ref:`parameter constants <parameter-constants>` in case of C API and in case of Server Thin client as raw strings. Please refer to examples in :doc:`C </cuopt-c/continuous/index>` and :doc:`Server Thin client </cuopt-server/index>` for more details.
 
 .. note::
    When setting parameters in thin client solver settings, remove ``CUOPT_`` from the parameter name and convert to lowercase. For example, ``CUOPT_TIME_LIMIT`` would be set as ``time_limit``.
 
-Parameters common to LP/MILP
-----------------------------
+Common Parameters
+-----------------
 
 We begin by describing parameters common to both the MILP and LP solvers
 
@@ -66,10 +65,6 @@ Presolve
 ``CUOPT_PRESOLVE`` controls which presolver to use for presolve reductions.
 cuOpt provides presolve reductions for linear programming (LP) problems using either PSLP or Papilo, and for mixed-integer programming (MIP) problems using Papilo. By default, Papilo presolve is always enabled for MIP problems. For LP problems, PSLP presolve is always enabled by default. You can explicitly control the presolver by setting this parameter to 0 (disable presolve), 1 (Papilo), or 2 (PSLP).
 
-Probing
-^^^^^^^
-``CUOPT_MIP_PROBING`` toggles the probing-cache step of cuOpt's internal MIP presolve. The probing pass evaluates variable fixings to discover implications used later by branch-and-bound and the rounding heuristics. It is enabled by default (``true``); set it to ``false`` to skip probing while leaving the rest of presolve in place. Setting ``CUOPT_PRESOLVE=0`` already turns off the entire presolve pipeline, so ``CUOPT_MIP_PROBING`` only matters when presolve is otherwise enabled. Probing is also skipped in deterministic mode and on LP-only solves.
-
 Dual Postsolve
 ^^^^^^^^^^^^^^
 ``CUOPT_DUAL_POSTSOLVE`` controls whether dual postsolve is enabled when using Papilo presolver for LP problems. Disabling dual postsolve can improve solve time at the expense of not having
@@ -375,184 +370,36 @@ The duality gap is computed as follows::
 
 .. note:: The default value is ``1e-4``.
 
+Primal Infeasibility Tolerance
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Mixed Integer Linear Programming
----------------------------------
-
-We now describe parameter settings for the MILP solvers
-
-
-Heuristics only
-^^^^^^^^^^^^^^^
-
-``CUOPT_MIP_HEURISTICS_ONLY`` controls if only the GPU heuristics should be run for the MIP problem. When set to true, only the primal
-bound is improved via the GPU. When set to false, both the GPU and CPU are used and
-the dual bound is improved on the CPU.
-
-.. note:: The default value is false.
-
-Scaling
-^^^^^^^
-
-``CUOPT_MIP_SCALING`` controls if scaling should be applied to the MIP problem.
-
-* ``0``: Scaling is off.
-* ``1``: Scaling is on.
-* ``2``: Scaling is not applied to the objective (default).
-
-Absolute Tolerance
-^^^^^^^^^^^^^^^^^^
-
-``CUOPT_MIP_ABSOLUTE_TOLERANCE`` controls the MIP absolute tolerance.
-
-.. note:: The default value is ``1e-6``.
-
-Relative Tolerance
-^^^^^^^^^^^^^^^^^^
-
-``CUOPT_MIP_RELATIVE_TOLERANCE`` controls the MIP relative tolerance.
-
-.. note:: The default value is ``1e-12``.
-
-
-Integrality Tolerance
-^^^^^^^^^^^^^^^^^^^^^
-
-``CUOPT_MIP_INTEGRALITY_TOLERANCE`` controls the MIP integrality tolerance. A variable is considered to be integral, if
-it is within the integrality tolerance of an integer.
-
-.. note:: The default value is ``1e-5``.
-
-Absolute MIP Gap
-^^^^^^^^^^^^^^^^
-
-``CUOPT_MIP_ABSOLUTE_GAP`` controls the absolute tolerance used to terminate the MIP solve. The solve terminates when::
-
-    Best Objective - Dual Bound  <= absolute tolerance
-
-when minimizing or
-
-    Dual Bound - Best Objective <= absolute tolerance
-
-when maximizing.
-
-.. note:: The default value is ``1e-10``.
-
-Relative MIP Gap
-^^^^^^^^^^^^^^^^
-
-``CUOPT_MIP_RELATIVE_GAP`` controls the relative tolerance used to terminate the MIP solve. The solve terminates when::
-
-    abs(Best Objective - Dual Bound) / abs(Best Objective) <= relative tolerance
-
-If the Best Objective and the Dual Bound are both zero the gap is zero. If the best objective value is zero, the
-gap is infinity.
-
-.. note:: The default value is ``1e-4``.
-
-
-Node Limit
-^^^^^^^^^^
-
-``CUOPT_NODE_LIMIT`` controls the maximum number of branch-and-bound nodes the MILP solver will explore before stopping and returning the current best feasible solution (if any). If set along with the time limit, cuOpt stops at whichever limit is hit first.
-
-.. note:: By default there is no node limit. The setting only affects MILP;
-   it is ignored for LP and QP.
+``CUOPT_PRIMAL_INFEASIBLE_TOLERANCE`` controls the tolerance used to detect primal infeasibility in PDLP.
+A certificate of primal infeasibility is accepted when the primal infeasibility residual falls below this threshold.
 
-Cut Passes
-^^^^^^^^^^
+.. note:: The default value is ``1e-8``.
 
-``CUOPT_MIP_CUT_PASSES`` controls the number of cut passes to run. Set this value to 0 to disable cuts. Set this value to larger numbers to perform more cut passes.
+Dual Infeasibility Tolerance
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. note:: The default value is ``10``.
+``CUOPT_DUAL_INFEASIBLE_TOLERANCE`` controls the tolerance used to detect dual infeasibility (unboundedness) in PDLP.
+A certificate of dual infeasibility is accepted when the dual infeasibility residual falls below this threshold.
 
-Mixed Integer Rounding Cuts
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. note:: The default value is ``1e-8``.
 
-``CUOPT_MIP_MIXED_INTEGER_ROUNDING_CUTS`` controls whether to use mixed integer rounding cuts.
-The default value of ``-1`` (automatic) means that the solver will decide whether to use mixed integer rounding cuts based on the problem characteristics.
-Set this value to 1 to enable mixed integer rounding cuts.
-Set this value to 0 to disable mixed integer rounding cuts.
+Barrier Iterative Refinement
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. note:: The default value is ``-1`` (automatic).
+``CUOPT_BARRIER_ITERATIVE_REFINEMENT`` controls whether iterative refinement is applied after each barrier iteration to improve solution accuracy.
 
-Mixed Integer Gomory Cuts
-^^^^^^^^^^^^^^^^^^^^^^^^^
+* ``0`` (``CUOPT_BARRIER_ITERATIVE_REFINEMENT_OFF``): Disable iterative refinement (default).
+* ``1`` (``CUOPT_BARRIER_ITERATIVE_REFINEMENT_ON``): Enable iterative refinement.
 
-``CUOPT_MIP_MIXED_INTEGER_GOMORY_CUTS`` controls whether to use mixed integer Gomory cuts.
-The default value of ``-1`` (automatic) means that the solver will decide whether to use mixed integer Gomory cuts based on the problem characteristics.
-Set this value to 1 to enable mixed integer Gomory cuts.
-Set this value to 0 to disable mixed integer Gomory cuts.
-
-.. note:: The default value is ``-1`` (automatic).
-
-Strong Chvatal-Gomory Cuts
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-``CUOPT_MIP_STRONG_CHVATAL_GOMORY_CUTS`` controls whether to use strong Chvatal-Gomory cuts.
-The default value of ``-1`` (automatic) means that the solver will decide whether to use strong Chvatal-Gomory cuts based on the problem characteristics.
-Set this value to 1 to enable strong Chvatal Gomory cuts.
-Set this value to 0 to disable strong Chvatal Gomory cuts.
-
-.. note:: The default value is ``-1`` (automatic).
-
-Knapsack Cuts
-^^^^^^^^^^^^^
-
-``CUOPT_MIP_KNAPSACK_CUTS`` controls whether to use knapsack cuts.
-The default value of ``-1`` (automatic) means that the solver will decide whether to use knapsack cuts based on the problem characteristics.
-Set this value to 1 to enable knapsack cuts.
-Set this value to 0 to disable knapsack cuts.
-
-.. note:: The default value is ``-1`` (automatic).
-
-
-Cut Change Threshold
-^^^^^^^^^^^^^^^^^^^^
-
-``CUOPT_MIP_CUT_CHANGE_THRESHOLD`` controls the threshold for the improvement in the dual bound per cut pass.
-Larger values require the dual bound to improve significantly in each cut pass.
-Set this value to 0 to allow the cut passes to continue even if the dual bound does not improve.
-
-.. note:: The default value is ``1e-3``.
-
-Cut Min Orthogonality
-^^^^^^^^^^^^^^^^^^^^^
-
-``CUOPT_MIP_CUT_MIN_ORTHOGONALITY`` controls the minimum orthogonality required for a cut to be added to the LP relaxation.
-Set this value close to 1, to require all cuts be close to orthogonal to each other.
-Set this value close to zero to allow more cuts to be added to the LP relaxation.
-
-.. note:: The default value is ``0.5``.
-
-Reduced Cost Strengthening
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-``CUOPT_MIP_REDUCED_COST_STRENGTHENING`` controls whether to use reduced-cost strengthening.
-When enabled, the solver will use integer feasible solutions to strengthen the bounds of integer variables.
-The default value of ``-1`` (automatic) means that the solver will decide whether to use reduced-cost strengthening based on the problem characteristics.
-Set this value to 0 to disable reduced-cost strengthening.
-Set this value to 1 to perform reduced-cost strengthening during the root cut passes.
-Set this value to 2 to perform reduced-cost strengthening during the root cut passes and after strong branching.
-
-.. note:: The default value is ``-1`` (automatic).
-
-Reliability Branching
-^^^^^^^^^^^^^^^^^^^^^
-
-``CUOPT_MIP_RELIABILITY_BRANCHING`` controls the reliability branching mode.
-The default value of ``-1`` (automatic) means that the solver will decide whether to use reliability branching, and the reliability branching factor, based on the problem characteristics.
-Set this value to 0 to disable reliability branching.
-Set this value to k > 0, to enable reliability branching. A variable will be considered reliable if it has been branched on k times.
-
-.. note:: The default value is ``-1`` (automatic).
+.. note:: The default value is ``0`` (off).
 
-Batch PDLP Strong Branching
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Barrier Step Scale
+^^^^^^^^^^^^^^^^^^^
 
-``CUOPT_MIP_BATCH_PDLP_STRONG_BRANCHING`` controls whether to use batched PDLP over Dual Simplex during strong branching at the root.
-When enabled, the solver evaluates multiple branching candidates simultaneously in a single batched PDLP solve rather than solving them in parallel using Dual Simplex. This can significantly reduce the time spent in strong branching if Dual Simplex is struggling.
-Set this value to 0 to disable batched PDLP strong branching.
-Set this value to 1 to enable batched PDLP strong branching.
+``CUOPT_BARRIER_STEP_SCALE`` controls the scaling factor applied to the step size in the barrier method.
+Values less than 1 result in more conservative (shorter) steps; values greater than 1 result in more aggressive steps.
 
-.. note:: The default value is ``0`` (disabled). This setting is ignored if the problem is not a MIP problem.
+.. note:: By default cuOpt selects the step scale automatically.