A Parallel Algorithm for Solving Large Convex Minimax Problems

We consider unconstrained minimax problem where the objective function is the maximum of a finite number of smooth convex functions. We present an iterative method to compute the optimal solution for the unconstrained convex finite minimax problem. The algorithm developed estimates the direction of steepest-descent rapidly and using Armijo’s condition proceeds towards the solution. Owing to the highly parallel nature of the algorithm, it is highly suitable for large minimax problems. Algorithm is implemented on Nvidia Tesla C1060 graphics card using CUDA and numerical comparisons with RGA & CFSQP are presented.


Editor(s):
Deb, Kalyanmoy
Published in:
Lecture Notes in Computer Science, 6475, 35-44
Presented at:
8th International Conference on Simulated Evolution and Learning (SEAL), Indian Institute of Technology, Kanpur, India, December 1-4, 2010
Year:
2010
Publisher:
Springer
ISSN:
0302-9743
Keywords:
Laboratories:


Note: The status of this file is: Involved Laboratories Only


 Record created 2011-04-01, last modified 2018-03-17

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