Abstract
A function mapping from ℛnto ℛ is called an SC1-function if it is diflerentiable and its derivative is semismooth. A convex SC1-minimization problem is a convex minimization problem with an SC1-objective function and linear constraints. Applications of such minimization problems include stochastic quadratic programming and minimax problems. In this paper, we present a globally and superlinearly convergent trust-region algorithm for solving such a problem. Numerical examples are given on the application of this algorithm to stochastic quadratic programs.
Original language | English |
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Pages (from-to) | 649-669 |
Number of pages | 21 |
Journal | Journal of Optimization Theory and Applications |
Volume | 90 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Jan 1996 |
Externally published | Yes |
Keywords
- Global convergence
- Stochastic quadratic programs
- Superlinear convergence
- Trust-region algorithms
ASJC Scopus subject areas
- Control and Optimization
- Management Science and Operations Research
- Applied Mathematics