Noise considerations in circuit optimization
Proceedings of the 1998 IEEE/ACM international conference on Computer-aided design
Gradient-based optimization of custom circuits using a static-timing formulation
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Overview of continuous optimization advances and applications to circuit tuning
Proceedings of the 2001 international symposium on Physical design
Uncertainty-aware circuit optimization
Proceedings of the 39th annual Design Automation Conference
CUTEr and SifDec: A constrained and unconstrained testing environment, revisited
ACM Transactions on Mathematical Software (TOMS)
Large-scale nonlinear optimization in circuit tuning
Future Generation Computer Systems
Large-scale nonlinear optimization in circuit tuning
Future Generation Computer Systems
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In this paper we propose extensions to trust-region algorithms in which the classical step is augmented with a second step that we insist yields a decrease in the value of the objective function. The classical convergence theory for trust-region algorithms is adapted to this class of two-step algorithms. The algorithms can be applied to any problem with variable(s) whose contribution to the objective function is a known functional form. In the nonlinear programming package LANCELOT, they have been applied to update slack variables and variables introduced to solve minimax problems, leading to enhanced optimization efficiency. Extensive numerical results are presented to show the effectiveness of these techniques.