Abstract
A geometric program (GP) is a type of mathematical optimization problem characterized by objective and constraint functions that have a special form. Recently developed solution methods can… Expand

A specialized interior-point method for solving large-scale -regularized LSPs that uses the preconditioned conjugate gradients algorithm to compute the search direction and can solve large sparse problems, with a million variables and observations, in a few tens of minutes on a PC.Expand

This paper proposes a variation on Hodrick-Prescott (H-P) filtering, a widely used method for trend estimation that substitutes a sum of absolute values for the sum of squares used in H-P filtering to penalize variations in the estimated trend.Expand

The problem of finding the (symmetric) edge weights that result in the least mean-square deviation in steady state is considered and it is shown that this problem can be cast as a convex optimization problem, so the global solution can be found efficiently.Expand

This paper describes an efficient interior-point method for solving large-scale l1-regularized logistic regression problems, and shows how a good approximation of the entire regularization path can be computed much more efficiently than by solving a family of problems independently.Expand

This paper considers the problem of finding the optimal kernel, over a given convex set of kernels, and shows that this optimal kernel selection problem can be reformulated as a tractable convex optimization problem which interior-point methods can solve globally and efficiently.Expand

This paper defines a rate maximization power allocation game in a frequency selective Gaus- sian interference channel, after assuming a suboptimal but pragmatic multi-user coding scheme. We show that… Expand

Abstract
The optimal solution of a geometric program (GP) can be sensitive to variations in the problem data. Robust geometric programming can systematically alleviate the sensitivity problem by… Expand

A method for digital circuit optimization based on formulating the problem as a geometric program or generalized geometric program (GGP), which can be transformed to a convex optimization problem and then very efficiently solved.Expand

It is shown that with general convex uncertainty models on the problem data, robust Fisher LDA can be carried out using convex optimization using a certain type of product form uncertainty model at a cost comparable to standard Fisher L DA.Expand