convex programming

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convex programming

[′kän‚veks ′prō‚gram·iŋ]
(mathematics)
Nonlinear programming in which both the function to be maximized or minimized and the constraints are appropriately chosen convex or concave functions of the independent variables.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
Due to the nonconvex problem of the objective function, we could not employ the convex optimization algorithm to obtain the optimal sensing time directly.
Therefore, the solution methods of convex optimization problem can not be used directly.
For antenna pattern synthesis, the methods are divided into three types: analytical method, swarm intelligence algorithm, and convex optimization algorithm.
In the low-sparsity approach, the attack vector can be constructed based on the convex optimization as a sparse term where only few components are nonzeros, which indicates that a small number of sensors need to be controlled.
For more generality, we first consider the following stochastic convex optimization problem:
The main contributions of the present study are as follows: (1) we reformulate the projections in the Gerchberg super-resolution algorithm using linear matrix equations, (2) we formulate a convex optimization problem, in which the reformulated projections and the low-TV/low-rank regularization are represented in a cost function and constraints, (3) we explicitly describe the algorithm for solving the convex optimization problem with the alternating direction method of multipliers (ADMM), and (4) we present extensive experimental evaluations conducted using the proposed method.
In the literature, analog design automation is an active research area, so that a couple of studies, which are convex optimization based [1]-[3] and utilized from genetic algorithm [4] were developed.
By weighting combination of the nuclear and the L1norms, a convenient convex optimization problem (principal component pursuit) was demonstrated, under suitable assumptions, to recover the low-rank and sparse components exactly of a given data-matrix (or video for our purposes).
Nedic, "Distributed random projection algorithm for convex optimization," IEEE Journal on Selected Topics in Signal Processing, vol.