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Combinatorial optimization tools for routing, scheduling, flows, and constraint programming.

An operator-splitting numerical solver for convex quadratic programs.

Parameter-efficient model adaptation methods for testing trainable-parameter budgets and adapter composition.

Linear and mixed-integer optimization modeling in Python with external solver interfaces.

Covariance-matrix adaptation evolution strategies for derivative-free numerical optimization.

Multi-objective optimization algorithms, operators, and analysis tools.

Algebraic optimization modeling for linear, integer, nonlinear, and other mathematical programs.

Finite-domain constraint satisfaction for combinatorial search problems in Python.

Tensor computation and automatic differentiation framework for constructing controlled learning and gradient experiments.

Training-loop framework for studying checkpoint restoration, reproducibility and separation of model logic from orchestration.

Distributed execution and machine-learning infrastructure for studying task scheduling, tuning and fault handling.

A splitting conic solver for numerical convex optimization.