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

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 algorith...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.116604-116611
Main Authors: Iqbal, Zafar, Nooshabadi, Saeid, Yamazaki, Ichitaro, Tomov, Stanimire, Dongarra, Jack
Format: Article
Language:English
Subjects:
Algorithms
Computational geometry
Convex analysis
Convex functions
Convex optimization
Convexity
Covariance matrices
Embedded systems
Karush–Kuhn–Tucker (KKT)
linear solver
Linear systems
Matrix decomposition
Optimization
Predictive control
Run time (computers)
Solvers
Symmetric matrices
Citations: Items that this one cites
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3106054