Paper Info
Title
PTrace: derivative-free local tracing of bicriterial design tradeoffs
Abstract
This paper presents a novel method, PTrace, to locally and uniformly trace convex bicriterial Pareto-optimal fronts for bicriterial optimization problems that, unlike existing methods, does not require derivatives of the objectives with respect to the design variables. The method computes a sequence of points along the front in a user-specified direction from a starting point, such that the points are roughly uniformly spaced as per a spacing constraint from the user. At each iteration, a local quadratic model of the front is used to estimate an appropriate weighted sum of objectives that, on optimization, will give the next point on the front. A single objective optimization on this weighted sum then generates the actual point, which is then used to build a new local model. The method uses convexity-based heuristics to improve on mildly sub-optimal results from the optimizer and reuses cached points to improve the optimization speed and quality. We test the method on a synthetic and a 6-T SRAM power-performance tradeoff test case to demonstrate its effectiveness.
Year
DOI
Venue
2011
10.1109/ICCAD.2011.6105375
ICCAD
Keywords
Field
DocType
Pareto optimisation,convex programming,iterative methods,6-T SRAM power-performance tradeoff test case,PTrace method,bicriterial design tradeoff,bicriterial optimization problem,cached points,convex bicriterial Pareto-optimal front tracing,convexity-based heuristics,derivative-free local tracing,design variables,local quadratic model,optimization quality,optimization speed,single objective optimization,spacing constraint,weighted sum
Mathematical optimization,Convexity,Iterative method,Computer science,Static analysis,Heuristics,Convex optimization,Optimization problem,Tracing,Cholesky decomposition
Conference
ISSN
ISBN
Citations 
1933-7760
978-1-4577-1398-9
1
PageRank 
References 
Authors
0.35
4
1
Name
Order
Citations
PageRank
Amith Singhee134722.94