Name
Abstract | ||
|---|---|---|
The convex weighted-sum method for multi-objective optimization has the desirable property of not worsening the difficulty of the optimization problem, but can lead to very nonuniform sampling. This paper explains the relationship between the weights and the partial derivatives of the tradeoff surface, and shows how to use it to choose the right weights and uniformly sample largely convex tradeoff surfaces. It proposes a novel method, Derivative Pursuit (DP), that iteratively refines a simplicial approximation of the tradeoff surface by using partial derivative information to guide the weights generation. We demonstrate the improvements offered by DP on both synthetic and circuit test cases, including a 22 nm SRAM bitcell design problem with strict read and write yield constraints, and power and performance objectives. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1837274.1837503 | DAC |
Keywords | Field | DocType |
Pareto optimisation,SRAM chips,convex programming,iterative methods,Pareto sampling,SRAM bitcell design,circuit test case,convex tradeoff surfaces,convex weighted sum method,derivative pursuit,multiobjective optimization problem,nonuniform sampling,partial derivative information,synthetic test cases,tradeoff surface,Multi-objective optimization,Pareto,derivative pursuit,tradeoff | Mathematical optimization,Iterative method,Computer science,Partial derivative,Multi-objective optimization,Sampling (statistics),Convex optimization,Optimization problem,Pareto principle,Nonuniform sampling | Conference |
ISSN | Citations | PageRank |
0738-100X | 3 | 0.38 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amith Singhee | 1 | 347 | 22.94 |
| Pamela Castalino | 2 | 3 | 0.38 |