0957-4174/$ see front matter 2009 Elsevier Ltd. A doi:10.1016/j.eswa.2009.10.019 * Corresponding author. Tel.: +98 2177240487; fax E-mail addresses: [email protected] (F. Bayat), eade [email protected] (A.A. Jalali), [email protected] In this paper, using the concepts of field theory and potential functions a sub-optimal non-parametric algorithm for clustering of convex and non-convex da...

In this paper, a characterization of convex fuzzy mappings is obtained. SupposeF C : ® F is a fuzzy mapping, whereC is a non-empty convex subset ofR andF is the set of all fuzzy numbers. With respect to the fuzzy-max order, F is convex if and only if it is both quasi-convex and intermediate-point convex. In this paper, a characterization of convex fuzzy mappings will be given. Specifically, it ...

In this work we propose a general procedure for estimating the global minima of mathematical programs given in the general form as follows: min P0 (t) s.t. Pi (t) ≤ 0 for i = 1, . . . , k (Π) , where the functions Pi : R n → R are n-dimensional polynomials which are supposed to be non convex. The theory behind the Method of Moments guarantees that all global minima of the non convex program (Π)...

This paper considers the problem of recovering a sparse signal representation according to a signal dictionary. This problem is usually formalized as a penalized least-squares problem in which sparsity is usually induced by a l1-norm penalty on the coefficient. Such an approach known as the Lasso or Basis Pursuit Denoising has been shown to perform reasonably well in some situations. However, i...

Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterparts in several sparse learning settings. However, solving the non-convex optimization problems associated with non-convex penalties remains a big challenge. A commonly used approach is the Multi-S...

Here we study non-convex composite optimization: first, a finite-sum of smooth but non-convex functions, and second, a general function that admits a simple proximal mapping. Most research on stochastic methods for composite optimization assumes convexity or strong convexity of each function. In this paper, we extend this problem into the non-convex setting using variance reduction techniques, ...