Exploiting Block Structures of KKT Matrices for Efficient Solution of Convex Optimization Problems

Zafar Iqbal, Saeid Nooshabadi, Ichitaro Yamazaki, Stanimire Tomov, Jack Dongarra

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Convex optimization solvers are widely used in the embedded systems that require sophisticated optimization algorithms including model predictive control (MPC). In this paper, we aim to reduce the online solve time of such convex optimization solvers so as to reduce the total runtime of the algorithm and make it suitable for real-time convex optimization. We exploit the property of the Karush–Kuhn–Tucker (KKT) matrix involved in the solution of the problem that only some parts of the matrix change during the solution iterations of the algorithm. Our results show that the proposed method can effectively reduce the runtime of the solvers.

Original languageEnglish
Pages (from-to)116604-116611
Number of pages8
JournalIEEE Access
Volume9
DOIs
StatePublished - 2021
Externally publishedYes

Funding

This work was supported by the National Science Foundation under Grant 1709069.

FundersFunder number
National Science Foundation
Directorate for Computer and Information Science and Engineering1709069

    Keywords

    • Convex optimization
    • Karush–Kuhn–Tucker (KKT)
    • embedded systems
    • linear solver

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