Neural networks and the Kriging method are compared for constructing £tness approximation models in evolutionary optimization algorithms. The two models are applied in an identica...
- This paper presents a methodology for concurrently optimizing an IC fabrication process and a standard cell library in order to maximize overall yield. The approach uses the Conc...
Arun N. Lokanathan, Jay B. Brockman, John E. Renau...
We consider parameterized convex optimization problems over the unit simplex, that depend on one parameter. We provide a simple and efficient scheme for maintaining an -approximat...
Abstract--We establish that the min-sum messagepassing algorithm and its asynchronous variants converge for a large class of unconstrained convex optimization problems, generalizin...
We present a combinatorial framework for the study of a natural class of distributed optimization problems that involve decisionmaking by a collection of n distributed agents in th...