Title :
Iterative ordinal optimization and its applications
Author :
Deng, Mei May ; Ho, Yu-chi
Author_Institution :
AT&T Labs., Holmdel, NJ, USA
Abstract :
Ordinal optimization (OO) is a method of speeding up the process of stochastic optimization via parametric simulation. However, OO still has limitations as it stands. One drawback is the fact that the search space for many problems can be huge due to combinatorial explosion. In this paper, we introduce the concept and procedure of iterative ordinal optimization to overcome this limitation. The key idea is to narrow or restrict the search to favor “good” subsets of the search space through limited sampling. It is in the spirit of traditional hill climbing except instead of moving from point to point in the search space, we move from one subset or search representation to another. As an illustration of the methodology, we study the famous unsolved Witsenhausen problem (1968). We find a solution that is 50% better than the best known solution
Keywords :
combinatorial mathematics; iterative methods; search problems; stochastic programming; combinatorial explosion; hill climbing; iterative ordinal optimization; parametric simulation; search space; stochastic optimization; Computational modeling; Engineering profession; Explosions; Optimization methods; Pediatrics; Performance evaluation; Sampling methods; Softening; Stochastic processes; Utility theory;
Conference_Titel :
Decision and Control, 1997., Proceedings of the 36th IEEE Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-4187-2
DOI :
10.1109/CDC.1997.652403