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   Spectral projected gradient methods: Review and perspectives  
   
نویسنده birgin e.g. ,martínez j.m. ,raydan m.
منبع journal of statistical software - 2014 - دوره : 60 - - کد همایش: - صفحه:1 -21
چکیده    Over the last two decades,it has been observed that using the gradient vector as a search direction in large-scale optimization may lead to efficient algorithms. the effectiveness relies on choosing the step lengths according to novel ideas that are related to the spectrum of the underlying local hessian rather than related to the standard decrease in the objective function. a review of these so-called spectral projected gradient methods for convex constrained optimization is presented. to illustrate the performance of these low-cost schemes,an optimization problem on the set of positive definite matrices is described. © 2014,american statistical association. all rights reserved.
کلیدواژه Convex constrained problems; Large scale problems; Nonmonotone line search; Spectral projected gradient methods
آدرس department of computer science,institute of mathematics and statistics,university of são paulo,são paulo,sp 05508-090, Brazil, department of applied mathematics,institute of mathematics,statistics and scientific computing,university of campinas,campinas,sp, Brazil, departamento de cómputo científico y estadística,universidad simón bolívar,ap. 89000,caracas,1080-a, Venezuela
 
     
   
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