An outer-approximation algorithm for a class of mixed-integer nonlinear programs
Mathematical Programming: Series A and B
Solving mixed integer nonlinear programs by outer approximation
Mathematical Programming: Series A and B
Integrating SQP and Branch-and-Bound for Mixed Integer Nonlinear Programming
Computational Optimization and Applications
Exploring relaxation induced neighborhoods to improve MIP solutions
Mathematical Programming: Series A and B
Mathematical Programming: Series A and B
A Feasibility Pump for mixed integer nonlinear programs
Mathematical Programming: Series A and B
FilMINT: An Outer Approximation-Based Solver for Convex Mixed-Integer Nonlinear Programs
INFORMS Journal on Computing
An algorithmic framework for convex mixed integer nonlinear programs
Discrete Optimization
Inexact solution of NLP subproblems in MINLP
Journal of Global Optimization
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In this paper, we describe the implementation of some heuristics for convex mixed integer nonlinear programs. The work focuses on three families of heuristics that have been successfully used for mixed integer linear programs: diving heuristics, the Feasibility Pump, and Relaxation Induced Neighborhood Search (RINS). We show how these heuristics can be adapted in the context of mixed integer nonlinear programming. We present results from computational experiments on a set of instances that show how the heuristics implemented help finding feasible solutions faster than the traditional branch-and-bound algorithm and how they help in reducing the total solution time of the branch-and-bound algorithm.