Convex optimization

About: Convex optimization is a research topic. Over the lifetime, 24906 publications have been published within this topic receiving 908795 citations. The topic is also known as: convex optimisation.

An Introduction to the Theory of Functional Equations and Inequalities: Cauchy's Equation and Jensen's Inequality

Abstract: Preliminaries- Set Theory- Topology- Measure Theory- Algebra- Cauchy's Functional Equation and Jensen's Inequality- Additive Functions and Convex Functions- Elementary Properties of Convex Functions- Continuous Convex Functions- Inequalities- Boundedness and Continuity of Convex Functions and Additive Functions- The Classes A, B, ?- Properties of Hamel Bases- Further Properties of Additive Functions and Convex Functions- Related Topics- Related Equations- Derivations and Automorphisms- Convex Functions of Higher Orders- Subadditive Functions- Nearly Additive Functions and Nearly Convex Functions- Extensions of Homomorphisms

Imaging via Compressive Sampling

Abstract: Image compression algorithms convert high-resolution images into a relatively small bit streams in effect turning a large digital data set into a substantially smaller one. This article introduces compressive sampling and recovery using convex programming.

An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems

Abstract: The a‐ne rank minimization problem, which consists of flnding a matrix of minimum rank subject to linear equality constraints, has been proposed in many areas of engineering and science. A speciflc rank minimization problem is the matrix completion problem, in which we wish to recover a (low-rank) data matrix from incomplete samples of its entries. A recent convex relaxation of the rank minimization problem minimizes the nuclear norm instead of the rank of the matrix. Another possible model for the rank minimization problem is the nuclear norm regularized linear least squares problem. This regularized problem is a special case of an unconstrained nonsmooth convex optimization problem, in which the objective function is the sum of a convex smooth function with Lipschitz continuous gradient and a convex function on a set of matrices. In this paper, we propose an accelerated proximal gradient algorithm, which terminates in O(1= p †) iterations with an †-optimal solution, to solve this unconstrained nonsmooth convex optimization problem, and in particular, the nuclear norm regularized linear least squares problem. We report numerical results for solving large-scale randomly generated matrix completion problems. The numerical results suggest that our algorithm is e‐cient and robust in solving large-scale random matrix completion problems. In particular, we are able to solve random matrix completion problems with matrix dimensions up to 10 5 each in less than 10 minutes on a modest PC.

Moments, Positive Polynomials And Their Applications

Abstract: . From a theoretical viewpoint, the GPM has developments and impact in var-ious area of Mathematics like algebra, Fourier analysis, functional analysis, operator theory, probabilityand statistics, to cite a few. In addition, and despite its rather simple and short formulation, the GPMhas a large number of important applications in various fields like optimization, probability, mathematicalfinance, optimal control, control and signal processing, chemistry, cristallography, tomography, quantumcomputing, etc.In its full generality, the GPM is untractable numerically. However when K is a compact basic semi-algebraic set, and the functions involved are polynomials (and in some cases piecewise polynomials orrational functions), then the situation is much nicer. Indeed, one can define a systematic numerical schemebased on a hierarchy of semidefinite programs, which provides a monotone sequence that converges tothe optimal value of the GPM. (A semidefinite program is a convex optimization problem which up toarbitrary fixed precision, can be solved in polynomial time.) Sometimes finite convergence may evenocccur.In the talk, we will present the semidefinite programming methodology to solve the GPM and describein detail several applications of the GPM (notably in optimization, probability, optimal control andmathematical finance).R´ef´erences[1] J.B. Lasserre, Moments, Positive Polynomials and their Applications, Imperial College Press, inpress.[2] J.B. Lasserre, A Semidefinite programming approach to the generalized problem of moments,Math. Prog. 112 (2008), pp. 65–92.