Dual Prediction-Correction Methods for Linearly Constrained Time-Varying Convex Programs

Devising efficient algorithms to solve continuously-varying strongly convex optimization programs is key in many applications, from control systems to signal processing and machine learning. In this context, solving means to find and track the optimizer trajectory of the continuously-varying convex...

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Vydané v:	IEEE transactions on automatic control Ročník 64; číslo 8; s. 3355 - 3361
Hlavný autor:	Simonetto, Andrea
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Asymptotic methods Asymptotic properties Computational geometry Convergence Convex analysis Convex functions Convexity Cost function Dual ascent Error correction Iterative algorithms Iterative methods Machine learning parametric programming Prediction algorithms prediction–correction methods Signal processing Signal processing algorithms time-varying convex optimization Tracking errors Trajectory Trajectory optimization
ISSN:	0018-9286, 1558-2523
On-line prístup:	Získať plný text
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