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Descent methods for inverse problems

✍ Scribed by Ian Knowles

Publisher
Elsevier Science
Year
2001
Tongue
English
Weight
459 KB
Volume
47
Category
Article
ISSN
0362-546X

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✦ Synopsis

Of fundamental importance in modelling is the problem of recovering coefficient functions in a differential equation from appropriate measurements on the solution; this is investigated by means of steepest descent techniques using certain new Banach space gradients.

πŸ“œ SIMILAR VOLUMES

FEASIBLE DESCENT CONE METHODS FOR INEQUA
FEASIBLE DESCENT CONE METHODS FOR INEQUALITY CONSTRAINED OPTIMIZATION PROBLEMS
✍ J. A. SNYMAN; N. STANDER πŸ“‚ Article πŸ“… 1996 πŸ› John Wiley and Sons 🌐 English βš– 784 KB

A new Feasible Descent Cone (FDC) method for constrained optimization, previously restricted to linear objectives, is here generalized to include non-linear objective functions as well. In the basic and exact algorithm a sequence of descent steps is taken through the interior of the feasible region

Two general methods for inverse optimiza
Two general methods for inverse optimization problems
✍ C. Yang; J. Zhang πŸ“‚ Article πŸ“… 1999 πŸ› Elsevier Science 🌐 English βš– 240 KB

We formulate a group of inverse optimization problems as a uniform LP model and provide two computation methods. One is a column generation method which generates necessary columns for simplex method by solving the original optimization problem. Another is an application of the ellipsoid method whic