Accelerated Algorithms for a Class of Optimization Problems with Equality and Box Constraints
Convex optimization with equality and inequality constraints is a ubiquitous problem in several optimization and control problems in large-scale systems. Recently there has been a lot of interest in establishing accelerated convergence of the loss function. A class of high-order tuners was recently...
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creator | Parashar, Anjali Srivastava, Priyank Annaswamy, Anuradha M |
description | Convex optimization with equality and inequality constraints is a ubiquitous
problem in several optimization and control problems in large-scale systems.
Recently there has been a lot of interest in establishing accelerated
convergence of the loss function. A class of high-order tuners was recently
proposed in an effort to lead to accelerated convergence for the case when no
constraints are present. In this paper, we propose a new high-order tuner that
can accommodate the presence of equality constraints. In order to accommodate
the underlying box constraints, time-varying gains are introduced in the
high-order tuner which leverage convexity and ensure anytime feasibility of the
constraints. Numerical examples are provided to support the theoretical
derivations. |
doi_str_mv | 10.48550/arxiv.2305.04433 |
format | Article |
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problem in several optimization and control problems in large-scale systems.
Recently there has been a lot of interest in establishing accelerated
convergence of the loss function. A class of high-order tuners was recently
proposed in an effort to lead to accelerated convergence for the case when no
constraints are present. In this paper, we propose a new high-order tuner that
can accommodate the presence of equality constraints. In order to accommodate
the underlying box constraints, time-varying gains are introduced in the
high-order tuner which leverage convexity and ensure anytime feasibility of the
constraints. Numerical examples are provided to support the theoretical
derivations.</description><identifier>DOI: 10.48550/arxiv.2305.04433</identifier><language>eng</language><subject>Computer Science - Learning ; Mathematics - Optimization and Control</subject><creationdate>2023-05</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2305.04433$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2305.04433$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Parashar, Anjali</creatorcontrib><creatorcontrib>Srivastava, Priyank</creatorcontrib><creatorcontrib>Annaswamy, Anuradha M</creatorcontrib><title>Accelerated Algorithms for a Class of Optimization Problems with Equality and Box Constraints</title><description>Convex optimization with equality and inequality constraints is a ubiquitous
problem in several optimization and control problems in large-scale systems.
Recently there has been a lot of interest in establishing accelerated
convergence of the loss function. A class of high-order tuners was recently
proposed in an effort to lead to accelerated convergence for the case when no
constraints are present. In this paper, we propose a new high-order tuner that
can accommodate the presence of equality constraints. In order to accommodate
the underlying box constraints, time-varying gains are introduced in the
high-order tuner which leverage convexity and ensure anytime feasibility of the
constraints. Numerical examples are provided to support the theoretical
derivations.</description><subject>Computer Science - Learning</subject><subject>Mathematics - Optimization and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz7tOwzAYhmEvDKhwAUz8N5Dg-JDDGKJykCqVoSuKfid2a8mJi22g5eopLdO3vPqkh5C7guailpI-YDjYr5xxKnMqBOfX5L0dBu10wKRHaN3WB5t2UwTjAyB0DmMEb2C9T3ayP5isn-EteOX0Kfo-tbD8-ERn0xFwHuHRH6Dzc0wB7ZziDbky6KK-_d8F2TwtN91Ltlo_v3btKsOy4lnBpBxLUVMzMEbLpm60MoOhTc0b1tSskqPkhahQUaa0UkKVuhC0EopyI4zgC3J_uT37-n2wE4Zj_-fsz07-C6IpTbo</recordid><startdate>20230507</startdate><enddate>20230507</enddate><creator>Parashar, Anjali</creator><creator>Srivastava, Priyank</creator><creator>Annaswamy, Anuradha M</creator><scope>AKY</scope><scope>AKZ</scope><scope>GOX</scope></search><sort><creationdate>20230507</creationdate><title>Accelerated Algorithms for a Class of Optimization Problems with Equality and Box Constraints</title><author>Parashar, Anjali ; Srivastava, Priyank ; Annaswamy, Anuradha M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a673-1255d6480fc2206989ebfcf09839298275d53147ab02bebb4b6e14074b03f4f43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Learning</topic><topic>Mathematics - Optimization and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Parashar, Anjali</creatorcontrib><creatorcontrib>Srivastava, Priyank</creatorcontrib><creatorcontrib>Annaswamy, Anuradha M</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Mathematics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Parashar, Anjali</au><au>Srivastava, Priyank</au><au>Annaswamy, Anuradha M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accelerated Algorithms for a Class of Optimization Problems with Equality and Box Constraints</atitle><date>2023-05-07</date><risdate>2023</risdate><abstract>Convex optimization with equality and inequality constraints is a ubiquitous
problem in several optimization and control problems in large-scale systems.
Recently there has been a lot of interest in establishing accelerated
convergence of the loss function. A class of high-order tuners was recently
proposed in an effort to lead to accelerated convergence for the case when no
constraints are present. In this paper, we propose a new high-order tuner that
can accommodate the presence of equality constraints. In order to accommodate
the underlying box constraints, time-varying gains are introduced in the
high-order tuner which leverage convexity and ensure anytime feasibility of the
constraints. Numerical examples are provided to support the theoretical
derivations.</abstract><doi>10.48550/arxiv.2305.04433</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Learning Mathematics - Optimization and Control |
title | Accelerated Algorithms for a Class of Optimization Problems with Equality and Box Constraints |
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