Geometric approach to Fletcher's ideal penalty function

A Geometric Approach to Fletcher's Ideal Penalty Function

In this note we derive a geometric formulation for equality constrained problems of an ideal penalty function This di erentiable penalty function requires no parameter esti mation or adjustment has numerical conditioning similar to that of the target function from which it is constructed and also has the desirable property that the strict second order constrained minima of the target function a...

Penalty Function Approach to Robust Analog IC Design

Automating the robust IC design process is becoming more and more important due to its complexity and decreasing time to market. In order for the circuit to be robust it must satisfy all design requirements across a range of operating conditions and manufacturing process variations. Part of the design process, which is performed by experienced analog IC designers, is automated. A transformation...

Wavelet regression: a penalty function approach

Wavelet thresholding as introduced by Donoho and Johnstone (1994) provides a simple technique which involves thresholding the output of the Discrete Wavelet Transform (DWT) of the data of interest, as a way of removing noise from the signal. They show that their estimators are asymptotically minimax for a wide range of norms and spaces. This convergence requires strong regularization or thresho...

Constrained Evolutionary Optimization - the penalty function approach

Capacity penalty due to ideal zero-forcing decision-feedback equalization

We compare the capacity CZF of an idealized zero-forcing decision-feedback equalization (ZF-DFE) system to the capacity C of the underlying Gaussian-noise channel. We find that, for strictly bandlimited channels, the capacity penalty gets vanishingly small as the signal power increases. For non-strictly bandlimited channels, on the other hand, the capacity difference C − CZF grows without bound...