Results 211 to 220 of about 97,958 (264)

Convex multiresolution analysis

Proceedings of Third International Symposium on Time-Frequency and Time-Scale Analysis (TFTS-96), 1998
A standard wavelet multiresolution analysis can be defined via a sequence of projection operators onto a monotone sequence of closed vector subspaces possessing suitable invariance properties. We propose an extension of this framework in which the linear projection operators are replaced by nonlinear retractions onto convex sets.
Patrick L. Combettes, Jean-Christophe Pesquet   +1 more
openaire   +1 more source

A convex analysis approach for convex multiplicative programming

Journal of Global Optimization, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rúbia M. Oliveira, Paulo A. V. Ferreira
openaire   +3 more sources

The analysis of convex blobs

Computer Graphics and Image Processing, 1972
The analysis of shape is not well understood. To further our understanding it seemsreasonable to concentrate on one aspect of the problem. This paper deals with the analysis of convex blobs. The aim of the analysis is to extract fragments of a blob which are perceptually meaningful. This is done by attributing to each point a set of neigh boring points
openaire   +1 more source

Abstract Convexity in Measure Theory and in Convex Analysis

Journal of Mathematical Sciences, 2003
The paper under report is a survey on the so-called ``abstract convex analysis'' and on some applications to optimization problems. If \(\Omega\) is a set and \(H\) is a class of functions from \(\Omega\) into \(\mathbb{R}\), a function \(f: \Omega\to\mathbb{R}\cup \{+\infty\}\) is called \(H\)-convex if it is the supremum of a family of functions ...
openaire   +2 more sources

Convex Functionals on Convex Sets and Convex Analysis

1985
Over the last 20 years, parallel to the theory of monotone operators, a calculus for the investigation of convex functionals designated by convex analysis has emerged, which allows one to solve a number of problems in a simple way. To this calculus belong: (α) The subgradient ∂F (a generalization of the classical concept of derivative).
openaire   +1 more source

Convex Analysis on the Hermitian Matrices

SIAM Journal on Optimization, 1996
There is growing interest in optimization problems with real symmetric matrices as variables. Generally the matrix functions involved are spectral: they depend only on the eigenvalues of the matrix. It is known that convex spectral functions can be characterized exactly as symmetric convex functions of the eigenvalues.
openaire   +1 more source

Analysis of Matrix-Convex Functions

Cybernetics and Systems Analysis, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Amirgalieva, S. N., Ostapenko, V. V., Ostapenko, E. V.   +2 more
openaire   +1 more source

Convex Analysis

1995
This chapter discusses the elements of convex analysis which are very important in the study of optimization problems. In section 2.1 the fundamentals of convex sets are discussed. In section 2.2 the subject of convex and concave functions is presented, while in section 2.3 generalizations of convex and concave functions are outlined.
openaire   +1 more source

Convexity Theorems in Harmonic Analysis

Journal of Lie Theory, 1991
Let \(G=KAN\) be an Iwasawa decomposition of a real semisimple Lie group \(G\) and \(L: G\to{\mathfrak a}={\mathcal L}(A)\) determined by \(g\in K \exp L(g)N\). Then Kostant's Convexity Theorem says that \(L(aK)=\text{conv}(W \log a)\) for \(a\in A\), where \(W\) is the Weyl group associated to \({\mathfrak g}\), \({\mathfrak a}\) and conv denotes the ...
openaire   +2 more sources