NonOpt, a C++ software package for minimizing locally Lipschitz objective functions, is presented. The software is intended primarily for minimizing objective functions that are nonconvex and/or nonsmooth. The package has implementations of two main algorithmic strategies: a gradient-sampling and a proximal-bundle method. Each algorithmic strategy can employ quasi-Newton techniques for accelerating convergence in practice. The main computational cost in each iteration is solving a subproblem with a quadratic objective function, a linear equality constraint, and bound constraints. The software contains dual active-set and interior-point subproblem solvers that are designed specifically for solving these subproblems efficiently. The results of numerical experiments with various test problems are provided to demonstrate the speed and reliability of the software.
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Lehigh ISE Tech Report 25T-005
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