Computational experience with globally convergent descent methods for large sparse systems of nonlinear equations
This paper is devoted to globally convergent Armijo-type descent methods for solving large sparse systems of nonlinear equations. These methods include the discrete Newtcin method and a broad class of Newton-like methods based on various approximations of the Jacobian matrix. We propose a general th...
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| Veröffentlicht in: | Optimization methods & software 1998-01, Vol.8 (3-4), p.201-223 |
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| container_title | Optimization methods & software |
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| creator | Luksan, L Vlcek, J |
| description | This paper is devoted to globally convergent Armijo-type descent methods for solving large sparse systems of nonlinear equations. These methods include the discrete Newtcin method and a broad class of Newton-like methods based on various approximations of the Jacobian matrix. We propose a general theory of global convergence together with a robust algorithm including a special restarting strategy. This algorithm is based cfn the preconditioned smoothed CGS method for solving nonsymmetric systems of linejtr equations. After reviewing 12 particular Newton-like methods, we propose results of extensive computational experiments. These results demonstrate high efficiency of tip proposed algorithm |
| doi_str_mv | 10.1080/10556789808805677 |
| format | Article |
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These methods include the discrete Newtcin method and a broad class of Newton-like methods based on various approximations of the Jacobian matrix. We propose a general theory of global convergence together with a robust algorithm including a special restarting strategy. This algorithm is based cfn the preconditioned smoothed CGS method for solving nonsymmetric systems of linejtr equations. After reviewing 12 particular Newton-like methods, we propose results of extensive computational experiments. 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These methods include the discrete Newtcin method and a broad class of Newton-like methods based on various approximations of the Jacobian matrix. We propose a general theory of global convergence together with a robust algorithm including a special restarting strategy. This algorithm is based cfn the preconditioned smoothed CGS method for solving nonsymmetric systems of linejtr equations. After reviewing 12 particular Newton-like methods, we propose results of extensive computational experiments. These results demonstrate high efficiency of tip proposed algorithm</description><subject>Armijo-Type Descent Methods</subject><subject>Computational Experiments</subject><subject>Conjugate Gradient Type Methods</subject><subject>Global Convergence</subject><subject>Newton-Like Methods Inexact Methods</subject><subject>Nonlinear Equations</subject><subject>Nonsymmetric Linear Systems</subject><subject>Residual Smoothing</subject><issn>1055-6788</issn><issn>1029-4937</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1998</creationdate><recordtype>article</recordtype><recordid>eNqFkDtPwzAUhSMEEqXwA9g8sQXsOo5tiQVVvKRKLDBHl-SmDXLs1HYo_fe4lK1CTOdI93z3lWWXjF4zqugNo0KUUmlFlaLJyaNswuhM54Xm8njnhchTQJ1mZyF8UEoLVpSTbD13_TBGiJ2zYAh-Deg7tDWSTRdXZGncOxizJbWzn-iXaCNpMNQ77TGuXBNI6zwxkGokDOBDkm2I2AfiWmKdNZ1F8ATX48-QcJ6dtGACXvzqNHt7uH-dP-WLl8fn-d0irwumYo4tzrRulGgAuQAq-UxyjSXqWnKF0OqatYXiBUMoRcnT6U3DpBaKiUJK4NPsat938G49YohV36XFjQGLbgzVTBaClyVNQbYP1t6F4LGtBt_14LcVo9XuudXBcxMj90xn0_k9bJw3TRVha5xvPdi6C4dUFb9iIm__Jfnfg78BjfuU1w</recordid><startdate>19980101</startdate><enddate>19980101</enddate><creator>Luksan, L</creator><creator>Vlcek, J</creator><general>Gordon and Breach Science Publishers</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>19980101</creationdate><title>Computational experience with globally convergent descent methods for large sparse systems of nonlinear equations</title><author>Luksan, L ; Vlcek, J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c418t-efe299d85dae35a0732739e6e9c738eaf9c1f48341ea6563898dd1795815477a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Armijo-Type Descent Methods</topic><topic>Computational Experiments</topic><topic>Conjugate Gradient Type Methods</topic><topic>Global Convergence</topic><topic>Newton-Like Methods Inexact Methods</topic><topic>Nonlinear Equations</topic><topic>Nonsymmetric Linear Systems</topic><topic>Residual Smoothing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Luksan, L</creatorcontrib><creatorcontrib>Vlcek, J</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology 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>Optimization methods & software</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Luksan, L</au><au>Vlcek, J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Computational experience with globally convergent descent methods for large sparse systems of nonlinear equations</atitle><jtitle>Optimization methods & software</jtitle><date>1998-01-01</date><risdate>1998</risdate><volume>8</volume><issue>3-4</issue><spage>201</spage><epage>223</epage><pages>201-223</pages><issn>1055-6788</issn><eissn>1029-4937</eissn><abstract>This paper is devoted to globally convergent Armijo-type descent methods for solving large sparse systems of nonlinear equations. 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| source | Taylor & Francis Journals Complete |
| subjects | Armijo-Type Descent Methods Computational Experiments Conjugate Gradient Type Methods Global Convergence Newton-Like Methods Inexact Methods Nonlinear Equations Nonsymmetric Linear Systems Residual Smoothing |
| title | Computational experience with globally convergent descent methods for large sparse systems of nonlinear equations |
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