Add default accuracy for each method in LP (#514)

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## Description
Add default accuracy for each method in LP

## Checklist

- [x] I am familiar with the [Contributing
Guidelines](https://github.com/NVIDIA/cuopt/blob/HEAD/CONTRIBUTING.md).
- Testing
   - [ ] New or existing tests cover these changes
   - [ ] Added tests
   - [ ] Created an issue to follow-up
   - [x] NA
- Documentation
   - [ ] The documentation is up to date with these changes
   - [x] Added new documentation
   - [ ] NA

Author: rgsl888prabhu

docs/cuopt/source/lp-features.rst

@@ -70,10 +70,19 @@ Method
 
 **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.
 
+.. note::
+   PDLP solves to 1e-4 relative accuracy by default.
+
 **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.
 
+.. note::
+   Barrier solves to 1e-8 relative accuracy by default.
+
 **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.
 
+.. note::
+   Dual Simplex solves to 1e-6 absolute accuracy by default.
+
 
 Crossover
 ---------

docs/cuopt/source/lp-milp-settings.rst

@@ -87,6 +87,12 @@ Method
 
 .. note:: The default method is ``Concurrent``.
 
+Default accuracy for each method:
+
+* PDLP solves to 1e-4 relative accuracy by default.
+* Barrier solves to 1e-8 relative accuracy by default.
+* Dual Simplex solves to 1e-6 absolute accuracy by default.
+
 C API users should use the constants defined in :ref:`method-constants` for this parameter.
 
 Server Thin client users should use the :class:`cuopt_sh_client.SolverMethod` for this parameter.

docs/cuopt/source/system-requirements.rst

@@ -26,7 +26,7 @@ Dependencies are installed automatically when using the pip and Conda installati
       - 12.0+
 
    * Python:
-      - >= 3.10.* and <= 3.12.*
+      - >= 3.10.* and <= 3.13.*
 
    * NVIDIA drivers:
       - 525.60.13+ (Linux)
@@ -68,9 +68,9 @@ Dependencies are installed automatically when using the pip and Conda installati
       - 100+ GB free space
 
    * CUDA:
-      - 12.9
+      - 13.0
 
-   * Latest NVIDIA drivers (570.42.01+)
+   * Latest NVIDIA drivers (580.65.06+)
 
    * OS:
       - Linux distributions with glibc>=2.28 (released in August 2018):
@@ -98,4 +98,4 @@ Thin-client for Self-Hosted
    - x86-64
    - ARM64
 
-* Python >= 3.10.x <= 3.12.x
+* Python >= 3.10.x <= 3.13.x