Paper Info
Title
Learning Binary Warm Starts For Multiparametric Mixed-Integer Quadratic Programming
Abstract
In this paper we propose a lightweight neural network architecture that is able to learn the binary components of the optimal solution of a class of multiparametric mixed-integer quadratic programming (MIQP) problems, such as those that arise from hybrid model predictive control formulations.The predictor provides a binary warm-start to a specifically designed branch and bound (B&B) algorithm to quickly discover an integer-feasible solution of the given MIQP, with the aim of reducing the overall solution time required to find the global optimal solution on line.
Year
DOI
Venue
2019
10.23919/ECC.2019.8795808
2019 18TH EUROPEAN CONTROL CONFERENCE (ECC)
Field
DocType
Citations 
Branch and bound,Mathematical optimization,Computer science,Model predictive control,Neural network architecture,Quadratic programming,Mixed integer quadratic programming,Binary number
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Daniele Masti100.68
Alberto Bemporad24353568.62