Scylla: a matrix-free fix-propagate-and-project heuristic for mixed-integer optimization

07/07/2023
by   Gioni Mexi, et al.
0

We introduce Scylla, a primal heuristic for mixed-integer optimization problems. It exploits approximate solves of the Linear Programming relaxations through the matrix-free Primal-Dual Hybrid Gradient algorithm with specialized termination criteria, and derives integer-feasible solutions via fix-and-propagate procedures and feasibility-pump-like updates to the objective function. Computational experiments show that the method is particularly suited to instances with hard linear relaxations.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/02/2021

Learning Primal Heuristics for Mixed Integer Programs

This paper proposes a novel primal heuristic for Mixed Integer Programs,...
research
06/14/2019

A feasibility pump algorithm embedded in an annealing framework

The feasibility pump algorithm is an efficient primal heuristic for find...
research
11/08/2018

A Primal Decomposition Method with Suboptimality Bounds for Distributed Mixed-Integer Linear Programming

In this paper we deal with a network of agents seeking to solve in a dis...
research
02/18/2021

Smart Feasibility Pump: Reinforcement Learning for (Mixed) Integer Programming

In this work, we propose a deep reinforcement learning (DRL) model for f...
research
08/20/2019

Optimization Bounds from the Branching Dual

We present a general method for obtaining strong bounds for discrete opt...
research
01/31/2019

On the statistical evaluation of algorithmic's computational experimentation with infeasible solutions

The experimental evaluation of algorithms results in a large set of data...
research
08/10/2020

SWITSS: Computing Small Witnessing Subsystems

Witnessing subsystems for probabilistic reachability thresholds in discr...

Please sign up or login with your details

Forgot password? Click here to reset