Efficient solutions for hybrid Markov decision processes specified through probabi...
Models and algorithms for nonlinear mixed integer problems (MINLP)
A sampling method for constrained nonsmooth optimization problems
| Full text | |
| Author(s): |
Francisco A.M. Gomes
[1]
Total Authors: 1
|
| Affiliation: | [1] Universidade Estadual de Campinas. IMECC. Department of Applied Mathematics - Brasil
Total Affiliations: 1
|
| Document type: | Journal article |
| Source: | COMPUTATIONAL & APPLIED MATHEMATICS; v. 26, n. 3, p. 337-379, 2007-00-00. |
| Abstract | |
A sequential quadratic programming algorithm for solving nonlinear programming problems is presented. The new feature of the algorithm is related to the definition of the merit function. Instead of using one penalty parameter per iteration and increasing it as the algorithm progresses, we suggest that a new point is to be accepted if it stays sufficiently below the piecewise linear function defined by some previous iterates on the (f,||C||2²)-space. Therefore, the penalty parameter is allowed to decrease between successive iterations. Besides, one need not to decide how to update the penalty parameter. This approach resembles the filter method introduced by Fletcher and Leyffer [Math. Program., 91 (2001), pp. 239-269], but it is less tolerant since a merit function is still used. Numerical comparison with standard methods shows that this strategy is promising. (AU) | |