Bump mip from 1.17.6 to 2.0.0

[dependabot]

Bumps mip from 1.17.6 to 2.0.0.

Changelog

Sourced from mip's changelog.

Changelog — python-mip 2.0

Changes since 1.17.1

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Performance

• LinExpr.add_var and xsum hot paths optimised (single dict operation instead of two; bypass dispatch overhead for the common Var-only case), cutting 11–21% off constraint-heavy model build times.
• HiGHS backend gained a CFFI C-array cache (matching CBC's bulk-add architecture) that accumulates pending columns/rows and flushes via Highs_addCols/Highs_addRows, plus name→index dicts to avoid unnecessary flushes on variable/constraint lookups.
• Anonymous variables no longer pay the str.format/str.encode cost of auto-generated var(N) names.

A new reproducible benchmark suite (benchmarks/, documented in docs/bench.rst) was added to measure and track these improvements — comparing python-mip/CBC, python-mip/HiGHS, and python-mip/Gurobi model-build speed across CPython 3.14 and PyPy 3.11, against raw gurobipy and highspy (high-level and batch-numpy APIs), on four problems (n-Queens, TSP, RCPSP, and the new Capacitated Facility Location Problem). Highlights:

• python-mip/Gurobi on PyPy is 2–4× faster than on CPython, depending on model structure, and remains the only way to drive Gurobi from PyPy at all (gurobipy has no PyPy wheel).
• For sparse, variable-heavy models python-mip/Gurobi is competitive with or faster than raw gurobipy even on CPython; for dense-constraint models gurobipy's own loop is faster on CPython, but PyPy closes much of that gap.
• Against highspy's own recommended vectorized numpy-array API, python-mip/HiGHS is competitive with or faster than it on sparser/irregular problems (n-Queens, TSP), while the vectorized API pulls ahead on dense, rectangular problems (e.g. CFLP).

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New Features

Model.add_vars() batch variable creation

Model.add_vars(n, ...) / VarList.add_vars(n, ...) create n variables in a single call, avoiding per-call dispatch overhead compared to n calls to add_var().

New LP solve controls: TRUNCATED status, max_iter, lp_method, lp_preprocess

• OptimizationStatus.TRUNCATED for LP solves stopped early by a time or iteration limit (populates primal/dual solution and objective bound whenever feasible, instead of reporting a misleading OPTIMAL/INFEASIBLE).
• Model.max_iter caps simplex/barrier iterations for LP solves, wired through CBC, HiGHS and Gurobi.
• Model.optimize(lp_method=...) selects dual simplex, primal simplex or IPM/barrier — added to HiGHS and Gurobi (CBC already had it).
• New CBC-specific LP_Method.RACING and LP_Method.RECOMMEND values: RACING runs several

... (truncated)

Commits

• 42b95c9 Release 2.0: CBC racingLP support, benchmark suite, LP solve controls
• 3d016ef chore: bump cbcbox requirement to >=2.935
• 0587467 chore: bump cbcbox requirement to >=2.934
• 319805a Switch back to cbcbox for the bundled CBC library
• a368899 ci: add Python 3.14 to test matrix
• 64610aa Migrate from cbcbox to mipster for the bundled CBC library (#424)
• 3f03388 Remove deprecated timeMode parameter for CBC compatibility
• ae7252e docs: put n first in RCPSP tables, fix CSS column selectors
• fb776f3 docs: right-align table numbers and merge RCPSP config tables
• 8a0b773 docs: add RCPSP benchmark, gurobipy comparison, and trim bench descriptions
• Additional commits viewable in compare view

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