A simple effective heuristic for embedded mixed-integer quadratic programming

In this paper, we propose a fast optimisation algorithm for approximately minimising convex quadratic functions over the intersection of affine and separable constraints (i.e. the Cartesian product of possibly nonconvex real sets). This problem class contains many NP-hard problems such as mixed-inte...

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Vydané v:International journal of control Ročník 93; číslo 1; s. 2 - 12
Hlavní autori: Takapoui, Reza, Moehle, Nicholas, Boyd, Stephen, Bemporad, Alberto
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Abingdon Taylor & Francis 02.01.2020
Taylor & Francis Ltd
Predmet:
ADMM
Algorithms
Cartesian coordinates
embedded applications
Embedded systems
heuristic
Hybrid electric vehicles
Integers
Mathematical analysis
Mixed-integer quadratic programming
Optimization
Power converters
Powertrain
Quadratic equations
Quadratic programming
ISSN:0020-7179, 1366-5820
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Shrnutí:In this paper, we propose a fast optimisation algorithm for approximately minimising convex quadratic functions over the intersection of affine and separable constraints (i.e. the Cartesian product of possibly nonconvex real sets). This problem class contains many NP-hard problems such as mixed-integer quadratic programming. Our heuristic is based on a variation of the alternating direction method of multipliers (ADMM), an algorithm for solving convex optimisation problems. We discuss the favourable computational aspects of our algorithm, which allow it to run quickly even on very modest computational platforms such as embedded processors. We give several examples for which an approximate solution should be found very quickly, such as management of a hybrid-electric vehicle drivetrain and control of switched-mode power converters. Our numerical experiments suggest that our method is very effective in finding a feasible point with small objective value; indeed, we see that in many cases, it finds the global solution.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0020-7179
1366-5820
DOI:10.1080/00207179.2017.1316016