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Exploiting Block Structures of KKT Matrices for Efficient Solution of Convex Optimization Problems
IEEE Access, 2021 Convex optimization solvers are widely used in the embedded systems that require sophisticated optimization algorithms including model predictive control (MPC).
Zafar Iqbal +4 more
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Distributed Online Convex Optimization With an Aggregative Variable [PDF]
IEEE Transactions on Control of Network Systems, 2020 This article investigates distributed online convex optimization in the presence of an aggregative variable without any global/central coordinators over a multiagent network.
Xiuxian Li, Xinlei Yi, Lihua Xie
semanticscholar +1 more source
Convex Optimization in Julia [PDF]
2014 First Workshop for High Performance Technical Computing in Dynamic Languages, 2014 This paper describes Convex, a convex optimization modeling framework in Julia. Convex translates problems from a user-friendly functional language into an abstract syntax tree describing the problem. This concise representation of the global structure of the problem allows Convex to infer whether the problem complies with the rules of disciplined ...
Stephen Boyd +5 more
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Optimization of Convex Risk Functions [PDF]
SSRN Electronic Journal, 2004 We consider optimization problems involving convex risk functions. By employing techniques of convex analysis and optimization theory in vector spaces of measurable functions, we develop new representation theorems for risk models, and optimality and duality theory for problems with convex risk functions.
Andrzej Ruszczyński, Alexander Shapiro
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Learning Convex Optimization Models [PDF]
IEEE/CAA Journal of Automatica Sinica, 2020 A convex optimization model predicts an output from an input by solving a convex optimization problem. The class of convex optimization models is large, and includes as special cases many well-known models like linear and logistic regression.
Akshay Agrawal +2 more
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Large-Scale Convex Optimization
, 2022 Starting from where a first course in convex optimization leaves off, this text presents a unified analysis of first-order optimization methods – including parallel-distributed algorithms – through the abstraction of monotone operators.
Ernest K. Ryu, W. Yin
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Applied Sciences, 2020 With the joint optimization of the electricity−gas−heating system (EGHS) attracting more and more attention, a distributed optimized scheduling framework for EGHS based on an improved alternating direction method of multipliers (ADMM ...
Hanxin Zhu +3 more
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, 2021 This textbook is based on lectures given by the authors at MIPT (Moscow), HSE (Moscow), FEFU (Vladivostok), V.I. Vernadsky KFU (Simferopol), ASU (Republic of Adygea), and the University of Grenoble-Alpes (Grenoble, France). First of all, the authors focused on the program of a two-semester course of lectures on convex optimization, which is given to ...
Vorontsova, Evgeniya +3 more
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RBCC Mid-section Combined Trajectory Optimization Method Based on Particle Swarm-Pseudospectral Convex Optimization [PDF]
Hangkong bingqiIn order to solve the problem of combined trajectory optimization of RBCC mid-section, a nested optimization method based on particle swarm-pseudospectral convex optimization is proposed.
Yang Yuxuan, Fei Wanghua, Liu Haili, Wang Peichen, Yan Xunliang
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Projections Onto Convex Sets (POCS) Based Optimization by Lifting [PDF]
, 2013 Two new optimization techniques based on projections onto convex space (POCS) framework for solving convex and some non-convex optimization problems are presented.
Bozkurt, A. +7 more
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