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A limited-memory multipoint symmetric secant method for bound constrained optimization
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DOI：10.1023/A:1021561204463.png)
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摘要
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A new algorithm for solving smooth large-scale minimization problems with bound constraints is introduced. The way of dealing with active constraints is similar to the one used in some recently introduced quadratic solvers. A limited-memory multipoint symmetric secant method for approximating the Hessian is presented. Positive-definiteness of the Hessian approximation is not enforced. A combination of trust-region and conjugate-gradient approaches is used to explore a useful negative curvature information. Global convergence is proved for a general model algorithm. Results of numerical experiments are presented.
Keyword:
large-scale optimization
box constraints
gradient projection
trust region
multipoint symmetric secant methods
global convergence
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