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.
References in periodicals archive ?
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.
Keywords: Power allocation, integrated data and energy transfer, sum-throughput, fair-throughput, convex optimization.
Murgovski, N,, Component sizing of a plug-in hybrid electric powertrain via convex optimization.
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).
As a core convex optimization is the basic problem in the centralized optimization, unconstrained distributed convex optimization is also one of the most general basic distributed optimization problem and research starting point.
A unique category of optimization problems identified as convex optimization problem can proficiently used to sort out the solution in many cases.
To overcome the above-mentioned difficulties, the purpose of the paper is to generalize ADMM for separable convex optimization in the real number domain to the complex number domain.
We show how recent advancements in convex optimization for machine learning yields positive answers to some of the above questions: there exists cases in which much more efficient algorithms exist for learning practically important concepts.
The book describes recently developed convex optimization theory and high efficient algorithms used in estimation fusion, which can be used to model parts of dynamic systems and to develop distributed fusion control algorithms based on a theory of feedback control.
By relaxing truth values to the continuous domain, PSL is able to solve inference tasks as an efficient convex optimization.
Based on derived analysis, we tackle the proposed robust algorithm by convex optimization technique.
So far, convex optimization problem is which people can further study.