Make README consistent with other docs

[rg20]

Description

README had stale info on python API and C API for QP

Issue

Checklist

• I am familiar with the Contributing Guidelines.
• Testing
  • New or existing tests cover these changes
  • Added tests
  • Created an issue to follow-up
  • NA
• Documentation
  • The documentation is up to date with these changes
  • Added new documentation
  • NA

Summary by CodeRabbit

• Documentation
  • Updated product docs to include Quadratic Programming (QP) alongside LP, MILP, and VRP.
  • Added QP to supported problem types in both C and Python APIs.
  • Expanded Python API documentation with an algebraic modeling description for constraints and objectives and minor wording clarifications.

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[coderabbitai]

📝 Walkthrough

Walkthrough

README.md updated to add Quadratic Programming (QP) to the product capabilities, C API, and Python API descriptions; the Python section also gained an algebraic modeling API description and minor wording adjustments to reflect QP support.

Changes

Cohort / File(s)	Summary
README documentation README.md	Added Quadratic Programming (QP) alongside LP/MILP/VRP in capability list; added QP to C API support; updated Python support to include LP, MILP, QP and added an Algebraic modeling Python API description; minor wording edits.

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~3 minutes

🚥 Pre-merge checks | ✅ 3

✅ Passed checks (3 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title accurately describes the main change: updating the README to align with other documentation regarding API support.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.

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📜 Recent review details

Configuration used: Path: .coderabbit.yaml

Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 69bbcb0 and 845627f.

📒 Files selected for processing (1)

• README.md

🧰 Additional context used

🧠 Learnings (4)

📓 Common learnings

Learnt from: CR
Repo: NVIDIA/cuopt PR: 0
File: .github/.coderabbit_review_guide.md:0-0
Timestamp: 2025-11-25T10:20:49.822Z
Learning: Applies to docs/**/*.{rst,md} : Document numerical tolerances, parameter ranges, and algorithm phase descriptions in API documentation when solver behavior or tolerances change

Learnt from: chris-maes
Repo: NVIDIA/cuopt PR: 500
File: cpp/tests/linear_programming/c_api_tests/c_api_test.c:1033-1048
Timestamp: 2025-12-06T00:22:48.638Z
Learning: In cuOPT's quadratic programming API, when a user provides a quadratic objective matrix Q via set_quadratic_objective_matrix or the C API functions cuOptCreateQuadraticProblem/cuOptCreateQuadraticRangedProblem, the API internally computes Q_symmetric = Q + Q^T and the barrier solver uses 0.5 * x^T * Q_symmetric * x. From the user's perspective, the convention is x^T Q x. For a diagonal Q with values [q1, q2, ...], the resulting quadratic terms are q1*x1^2 + q2*x2^2 + ...

📚 Learning: 2025-10-22T14:25:22.899Z

Learnt from: aliceb-nv
Repo: NVIDIA/cuopt PR: 527
File: cpp/src/mip/diversity/lns/rins.cu:167-175
Timestamp: 2025-10-22T14:25:22.899Z
Learning: In MIP (Mixed Integer Programming) problems in the cuOPT codebase, `n_integer_vars == 0` is impossible by definition—MIP problems must have at least one integer variable. If there are no integer variables, it would be a pure Linear Programming (LP) problem, not a MIP problem.

Applied to files:

• README.md

📚 Learning: 2025-12-06T00:22:48.638Z

Learnt from: chris-maes
Repo: NVIDIA/cuopt PR: 500
File: cpp/tests/linear_programming/c_api_tests/c_api_test.c:1033-1048
Timestamp: 2025-12-06T00:22:48.638Z
Learning: In cuOPT's quadratic programming API, when a user provides a quadratic objective matrix Q via set_quadratic_objective_matrix or the C API functions cuOptCreateQuadraticProblem/cuOptCreateQuadraticRangedProblem, the API internally computes Q_symmetric = Q + Q^T and the barrier solver uses 0.5 * x^T * Q_symmetric * x. From the user's perspective, the convention is x^T Q x. For a diagonal Q with values [q1, q2, ...], the resulting quadratic terms are q1*x1^2 + q2*x2^2 + ...

Applied to files:

• README.md

📚 Learning: 2025-11-25T10:20:49.822Z

Learnt from: CR
Repo: NVIDIA/cuopt PR: 0
File: .github/.coderabbit_review_guide.md:0-0
Timestamp: 2025-11-25T10:20:49.822Z
Learning: Applies to **/*.cu : Detect inefficient GPU kernel launches with low occupancy or poor memory access patterns; optimize for coalesced memory access and minimize warp divergence in hot paths

Applied to files:

• README.md

🪛 markdownlint-cli2 (0.18.1)

README.md

28-28: Unordered list indentation
Expected: 2; Actual: 4

(MD007, ul-indent)

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30-30: Unordered list indentation
Expected: 2; Actual: 4

(MD007, ul-indent)

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32-32: Unordered list indentation
Expected: 2; Actual: 4

(MD007, ul-indent)

---

33-33: Unordered list indentation
Expected: 2; Actual: 4

(MD007, ul-indent)

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34-34: Unordered list indentation
Expected: 4; Actual: 8

(MD007, ul-indent)

🔇 Additional comments (3)

README.md (3)

14-14: LGTM! QP added to main description.

The addition of quadratic programming to the main product description is clear and consistent with the other supported problem types.

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28-28: LGTM! QP support documented correctly.

The additions of QP to C API and Python support sections are accurate and well-documented. The algebraic modeling API description provides helpful context for users.

Also applies to: 33-34

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36-38: The review comment is incorrect. Server does not support QP, so the current README.md is accurate.

The documentation consistently shows Server only supports LP, MILP, and Routing. The introduction.rst explicitly lists these three problem types for Server support. Server examples include only lp-examples.rst, milp-examples.rst, and routing-examples.rst—with no QP examples. The reference to lp-qp-milp-settings.rst in Server documentation is merely a link to shared parameter documentation, not an indicator that Server actually implements QP solving. The release notes explicitly state that QP support was added to the C and Python APIs, but not the Server.

Likely an incorrect or invalid review comment.

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[cwilkinson76]

LGTM

[rgsl888prabhu]

/ok to test 69bbcb0

[rg20]

/ok to test 845627f

[rgsl888prabhu]

/merge