期刊
IMA JOURNAL OF NUMERICAL ANALYSIS
卷 23, 期 4, 页码 539-559出版社
OXFORD UNIV PRESS
DOI: 10.1093/imanum/23.4.539
关键词
convex constrained optimization; projected gradient; nonmonotone line search; spectral gradient; Dykstra's algorithm
A new method is introduced for large-scale convex constrained optimization. The general model algorithm involves, at each-iteration, the approximate minimization of a convex quadratic on the feasible set of the original problem and global convergence is obtained by means of nonmonotone line searches. A specific algorithm, the Inexact Spectral Projected Gradient method (ISPG), is implemented using inexact projections computed by Dykstra's alternating projection method and generates interior iterates. The ISPG method is a generalization of the Spectral Projected Gradient method (SPG), but can be used when projections are difficult to compute. Numerical results for constrained least-squares rectangular matrix problems are presented.
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