We give a simple proof for the sample complexity bound O (1/ε4) of absolute approximation of MAX-CUT. The proof depends on a new analysis method for linear programs (LPs) underlying MAX-CUT which could be also of independent interest.

A high automation degree is one of the most important features of data driven modeling tools and it should be taken into consideration in classification systems design. In this regard, constructive training algorithms are essential to improve the automation degree of a modeling system. Among neuro-fuzzy classifiers, Simpson's (1992) min-max networks have the advantage of being trained in a cons...

We consider the problems of set partitioning into k clusters with minimum total cost and minimum of the maximum cost of a cluster. The cost function is given by an oracle, and we assume that it satisfies some natural structural constraints. That is, we assume that the cost function is monotone, the cost of a singleton is zero, and we assume that for all S ∩ S′ 6= ∅ the following holds c(S) + c(...

In a dynamic network, the quickest path problem asks for a path such that a given amount of flow can be sent from source to sink via this path in minimal time. In practical settings, for example, in evacuation or transportation planning, the problem parameters might not be known exactly a priori. It is therefore of interest to consider robust versions of these problems in which travel times and...

Min-max systems, or min-max-plus systems are algebraic models of Discrete Event Dynamic Systems. They are non-linear extension of the well known linear max-plus system models. In this paper, we survey our recent research results related to the structural property ”inseparability” of min-max systems proposed in [24]. It turns out inseparability has very interesting relationship to many aspects o...

Two Primal-dual interior point algorithms are presented for the problem of maximizing the smallest eigenvalue of a symmetric matrix over diagonal perturbations. These algorithms prove to be simple, robust, and eecient. Both algorithms are based on transforming the problem to one over the cone of positive semideenite matrices. One of the algorithms does this transformation through an intermediat...

We propose a new family of latent variable models called max-margin min-entropy (m3e) models, which define a distribution over the output and the hidden variables conditioned on the input. Given an input, an m3e model predicts the output with the smallest corresponding Rényi entropy of generalized distribution. This is equivalent to minimizing a score that consists of two terms: (i) the negativ...

This the problem maximum asymptotic bias of regression estimates over a-contamination for the joint of the response carriers. Two classes of estimates are treated: (1) Msestimates with bounded function p applied to the scaled residuals, using a very general class of scale estimates, and (2) Bounded influence function type generalized M-estimates. Estimates in the first class are oblta1rled as p...

Domain decomposition is one of the most eeective and popular parallel computing techniques for solving large scale numerical systems. In the special case when the amount of computation in a subdomain is proportional to the volume of the subdomain, domain decomposition amounts to minimizing the surface area of each subdomain while dividing the volume evenly. Motivated by this fact, we study the ...