SPHERICAL PROGRAMMING: A CONVEX PROGRAMMING ALGORITHM.
TEXAS A AND M UNIV COLLEGE STATION INST OF STATISTICS
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A new algorithm is developed for solving the problem of maximizing a function, fx, of n variables subject to m linear inequality constraints. The procedure consists of solving a sequence of subproblems which require the maximization of fx over a hypersphere. A simple algorithm is developed for solving the subproblems. The sequence of subproblem optima is shown to converge to the constrained optimum of fx if fx is concave. A discussion is given of our limited computational experience with the algorithm. Author
- Operations Research