Conference proceeding
A feasible direction subgradient algorithm for a class of nondifferentiable optimization problems
1979 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, Vol.2, pp.439-444
12/1979
DOI: 10.1109/CDC.1979.270212
Abstract
We present an implementable feasible direction subgradient algorithm for minimizing the maximum of a finite collection of functions subject to constraints. It is assumed that each function involved in defining the objective function is the sum of a finite collection of basic convex functions and that the number of different subgradient sets associated with nondifferentiable points of each basic function is finite on any bounded set. It is demonstrated that under certain conditions, including continuous differentiability of the constraints and a regularity condition of the µ feasible region, that the algorithm generates a feasible sequence which converges to an ε-optimal solution. Computational results for some example problems are included.
Details
- Title: Subtitle
- A feasible direction subgradient algorithm for a class of nondifferentiable optimization problems
- Creators
- Jacques Chatelon - ITT, Paris, FranceDonald Hearn - University of FloridaTimothy J. Lowe - University of Iowa, Business Analytics
- Resource Type
- Conference proceeding
- Publication Details
- 1979 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, Vol.2, pp.439-444
- DOI
- 10.1109/CDC.1979.270212
- Publisher
- IEEE
- Language
- English
- Date published
- 12/1979
- Academic Unit
- Business Analytics
- Record Identifier
- 9984963210302771
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