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We investigate the optimal configurations of n points on the unit sphere for a class of potential functions. In particular, we characterize these optimal configurations in terms of theirapproximation ...
The minimum `1-norm solution to an underdetermined system of linear equations y = Ax, is often, remarkably, also the sparsest solution to that system. This sparsity-seeking property is of interest i...
A number of problems in the analysis and design of control systems may be reformulated as the problem of minimizing the largest generalized eigenvalue of a pair of symmetric matrices which depend affi...
It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constraine...
We consider the NP-hard problem of minimizing a convex quadratic function over the integer lattice Zn. We present a semidefinite programming (SDP) method for obtaining a nontrivial lower bound on the ...
We present an ordinary differential equations approach to the analysis of algorithms for constructing l1 minimizing solutions to underdeter mined linear systems of full rank. It involves a relaxed min...
It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constraine...
We consider the fundamental problem of estimating the mean of a vector y = Xβ + z, where X is an n× p design matrix in which one can have far more variables than observations and z is a stochastic err...
This article considers the problem of \min_{\xi}\vec{Pr}(\vert \xi^Tx\vert\leq \alpha)\quad s.t.\quad \Vert\xi\Vert_2=1. We first concentrate on the case that x_i,i=1,\cdots,n are binary random variab...
Abstract: The simplified Lennard-Jones (LJ) potential minimization problem is $f(x)=4\sum_{i=1}^N \sum_{j=1,jto} x\in \mathbb{R}^n,$ where...
Abstract: This paper is concerned with the numerical minimization of energy functionals in $BV(\Omega)$ (the space of bounded variation functions) involving total variation for gray-scale 1-dimensiona...
Recent years have witnessed the popularity of using rank minimization as a regularizer for various signal processing and machine learning problems.
We look for the minimizers of the functional J( ) = | | − P( ) among planar convex domains constrained to lie into a given ring.
In this paper we consider the issue of energy efficiency in random access networks and show that optimizing transmission probabilities of nodes can enhance network performance in terms of energy cons...
It is an efficient and effective strategy to utilize the nuclear norm approximation to learn low-rank matrices, which arise frequently in machine learning and computer vision. So the exploration of n...

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