Global convergence of a robust filter SQP algorithm
We present a robust filter SQP algorithm for solving constrained optimization problems. This algorithm is based on the modified quadratic programming proposed by Burke to avoid the infeasibility of the quadratic programming subproblem at each iteration. Compared with other filter SQP algorithms, our...
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| Veröffentlicht in: | European journal of operational research 2010-10, Vol.206 (1), p.34-45 |
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| container_title | European journal of operational research |
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| creator | Shen, Chungen Xue, Wenjuan Chen, Xiongda |
| description | We present a robust filter SQP algorithm for solving constrained optimization problems. This algorithm is based on the modified quadratic programming proposed by Burke to avoid the infeasibility of the quadratic programming subproblem at each iteration. Compared with other filter SQP algorithms, our algorithm does not require any restoration phase procedure which may spend a large amount of computation. The main advantage of our algorithm is that it is globally convergent without requiring strong constraint qualifications, such as Mangasarian–Fromovitz constraint qualification (MFCQ) and the constant rank constraint qualification (CRCQ). Furthermore, the feasible limit points of the sequence generated by our algorithm are proven to be the KKT points if some weaker conditions are satisfied. Numerical results are also presented to show the efficiency of the algorithm. |
| doi_str_mv | 10.1016/j.ejor.2010.02.031 |
| format | Article |
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This algorithm is based on the modified quadratic programming proposed by Burke to avoid the infeasibility of the quadratic programming subproblem at each iteration. Compared with other filter SQP algorithms, our algorithm does not require any restoration phase procedure which may spend a large amount of computation. The main advantage of our algorithm is that it is globally convergent without requiring strong constraint qualifications, such as Mangasarian–Fromovitz constraint qualification (MFCQ) and the constant rank constraint qualification (CRCQ). Furthermore, the feasible limit points of the sequence generated by our algorithm are proven to be the KKT points if some weaker conditions are satisfied. 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This algorithm is based on the modified quadratic programming proposed by Burke to avoid the infeasibility of the quadratic programming subproblem at each iteration. Compared with other filter SQP algorithms, our algorithm does not require any restoration phase procedure which may spend a large amount of computation. The main advantage of our algorithm is that it is globally convergent without requiring strong constraint qualifications, such as Mangasarian–Fromovitz constraint qualification (MFCQ) and the constant rank constraint qualification (CRCQ). Furthermore, the feasible limit points of the sequence generated by our algorithm are proven to be the KKT points if some weaker conditions are satisfied. Numerical results are also presented to show the efficiency of the algorithm.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Comparative analysis</subject><subject>Constrained optimization</subject><subject>Convergence</subject><subject>CPLD</subject><subject>Exact sciences and technology</subject><subject>Filter</subject><subject>Filter SQP Constrained optimization CPLD</subject><subject>Mathematical programming</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Optimization algorithms</subject><subject>Quadratic programming</subject><subject>SQP</subject><subject>Studies</subject><issn>0377-2217</issn><issn>1872-6860</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNp9kFFrHCEUhSU0kG3aP9CnodDH2dyro47QlxLaJBBIStpncZw7icPsutXZhfz7OtmQxwhHRc45Xj7GviCsEVBdjGsaY1pzKA_A1yDwhK2w1bxWrYIPbAVC65pz1GfsY84jAKBEuWLiaoqdmyoftwdKj7T1VMWhclWK3T7P1RCmmVL18Pu-ctNjTGF-2nxip4ObMn1-Pc_Z318__1xe17d3VzeXP25r33Ax19oLKaXRZJAPYGTnPfaNUtSSNpqj6xvfm05D54zSINtWIiJprqQynVHinH099u5S_LenPNsx7tO2fGk5NGi0EIuJH00-xZwTDXaXwsalZ4tgFzZ2tAsbu7CxwG1hU0I3x1CiHfm3BJVVrJTtwQrHQZX9-eVWosKFIizaFYnGNtI-zZvS9e11Spe9m4bktj7kt07OFTccoPi-H31UkB0CJZt9WHj3IZGfbR_DeyP_BwQ_kBk</recordid><startdate>20101001</startdate><enddate>20101001</enddate><creator>Shen, Chungen</creator><creator>Xue, Wenjuan</creator><creator>Chen, Xiongda</creator><general>Elsevier B.V</general><general>Elsevier</general><general>Elsevier Sequoia S.A</general><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20101001</creationdate><title>Global convergence of a robust filter SQP algorithm</title><author>Shen, Chungen ; Xue, Wenjuan ; Chen, Xiongda</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c423t-7c355597e912f095bcc1d466e8e79721ad4cd9b70ba96705885111e726569b963</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Comparative analysis</topic><topic>Constrained optimization</topic><topic>Convergence</topic><topic>CPLD</topic><topic>Exact sciences and technology</topic><topic>Filter</topic><topic>Filter SQP Constrained optimization CPLD</topic><topic>Mathematical programming</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Optimization algorithms</topic><topic>Quadratic programming</topic><topic>SQP</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shen, Chungen</creatorcontrib><creatorcontrib>Xue, Wenjuan</creatorcontrib><creatorcontrib>Chen, Xiongda</creatorcontrib><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>European journal of operational research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shen, Chungen</au><au>Xue, Wenjuan</au><au>Chen, Xiongda</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Global convergence of a robust filter SQP algorithm</atitle><jtitle>European journal of operational research</jtitle><date>2010-10-01</date><risdate>2010</risdate><volume>206</volume><issue>1</issue><spage>34</spage><epage>45</epage><pages>34-45</pages><issn>0377-2217</issn><eissn>1872-6860</eissn><coden>EJORDT</coden><abstract>We present a robust filter SQP algorithm for solving constrained optimization problems. This algorithm is based on the modified quadratic programming proposed by Burke to avoid the infeasibility of the quadratic programming subproblem at each iteration. Compared with other filter SQP algorithms, our algorithm does not require any restoration phase procedure which may spend a large amount of computation. The main advantage of our algorithm is that it is globally convergent without requiring strong constraint qualifications, such as Mangasarian–Fromovitz constraint qualification (MFCQ) and the constant rank constraint qualification (CRCQ). Furthermore, the feasible limit points of the sequence generated by our algorithm are proven to be the KKT points if some weaker conditions are satisfied. Numerical results are also presented to show the efficiency of the algorithm.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.ejor.2010.02.031</doi><tpages>12</tpages></addata></record> |
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| ispartof | European journal of operational research, 2010-10, Vol.206 (1), p.34-45 |
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| language | eng |
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| source | RePEc; Elsevier ScienceDirect Journals Complete |
| subjects | Algorithms Applied sciences Comparative analysis Constrained optimization Convergence CPLD Exact sciences and technology Filter Filter SQP Constrained optimization CPLD Mathematical programming Operational research and scientific management Operational research. Management science Optimization algorithms Quadratic programming SQP Studies |
| title | Global convergence of a robust filter SQP algorithm |
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