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S. K. Neogy

Bio: S. K. Neogy is an academic researcher from Indian Statistical Institute. The author has contributed to research in topics: Linear complementarity problem & Complementarity theory. The author has an hindex of 10, co-authored 44 publications receiving 576 citations. Previous affiliations of S. K. Neogy include Indian Statistical Institute, Delhi Centre.

Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors consider the class of the invex functions introduced by Hanson and show that under certain conditions, a function defined on a set of non-invex sets A is pre-quasi-inverse on A.

365 citations

Journal ArticleDOI
TL;DR: This paper forms this problem as a linear complementarity problem with a square matrixM, a formulation which is different from a similar formulation given earlier by Lemke, and shows that the class of vertical block matrices which Cottle and Dantzig's algorithm can process is the same as theclass of equivalent square matrices in Lemke's algorithm.
Abstract: Given a vertical block matrixA, we consider in this paper the generalized linear complementarity problem VLCP(q, A) introduced by Cottle and Dantzig. We formulate this problem as a linear complementarity problem with a square matrixM, a formulation which is different from a similar formulation given earlier by Lemke. Our formulation helps in extending many well-known results in linear complementarity to the generalized linear complementarity problem. We also show that the class of vertical block matrices which Cottle and Dantzig's algorithm can process is the same as the class of equivalent square matrices which Lemke's algorithm can process. We also present some degree-theoretic results on a vertical block matrix.

43 citations

Journal ArticleDOI
TL;DR: In this article, the authors considered positive subdefinite matrices (PSBD) and showed that linear complementarity problems with PSBD matrices of rank ⩾ 2 are processable by Lemke's algorithm and that a PSBD matrix of rank 2 belongs to the class of sufficient matrices introduced by R.W. Cottle et al.

35 citations

Journal ArticleDOI
TL;DR: In this paper, the authors considered the class C 0 f of fully copositive matrices and the class E 0f of fully semimonotone matrices, and they showed that the columns of these matrices with positive diagonal entries are column sufficient.

30 citations

BookDOI
01 Apr 2008
TL;DR: In this article, the authors present an analysis of sets of Constraints, Traveling Salesman Problem, and Tolerance-Based Algorithms for linear programs with Totally Unimodular Coefficient Matrix Interior Point Method for Convex Quadratic Programming Analysis of Sets of CONstraints.
Abstract: Mathematical Programming and Its Applications in Finance Linear Programs with Totally Unimodular Coefficient Matrix Interior Point Method for Convex Quadratic Programming Analysis of Sets of Constraints, Traveling Salesman Problem and Tolerance-Based Algorithms Pedigree Polytope One-Defective Vertex Coloring Problem Complementarity Problem Fuzzy Twin Support Vector Machines for Pattern Classification Minimum Sum of Absolute Errors Regression Hedging Against the Market with No Short Selling Mathematical Programming and Electrical Network Analysis Dynamic Optimal Control Policy Forecasting for Supply Chain and Portfolio Management Variational Analysis in Bilevel Programming Game Engineering Games of Connectivity Robust Feedback Nash Equilibrium De Facto Delegation and Proposer Rules Bargaining Set in Effectivity Function Dynamic Oligopoly as a Mixed Large Game -- Toy Market, Balanced Games, Market Equilibrium for Combinatorial Auctions and the Matching Core of Non-negative TU Games Continuity, Manifolds and Arrow's Social Choice Problem Mixture Class of Stochastic Games.

28 citations


Cited by
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Journal ArticleDOI
TL;DR: A novel classification framework is proposed that provides a full picture of current literature on where and how BDA has been applied within the SCM context and reveals a number of research gaps, which leads to future research directions.

329 citations

Journal ArticleDOI
TL;DR: The method described, which is a variation on the K-means algorithm for clustering, seems to work well in practice, at least on data that can be fit well by a convex function.
Abstract: We consider the problem of fitting a convex piecewise-linear function, with some specified form, to given multi-dimensional data. Except for a few special cases, this problem is hard to solve exactly, so we focus on heuristic methods that find locally optimal fits. The method we describe, which is a variation on the K-means algorithm for clustering, seems to work well in practice, at least on data that can be fit well by a convex function. We focus on the simplest function form, a maximum of a fixed number of affine functions, and then show how the methods extend to a more general form.

220 citations

Journal ArticleDOI
TL;DR: In this article, several kinds of invariant monotone maps and generalized invariant maps are introduced, and relations between generalized monotonicity and generalized invexity are established, which are generalizations of those presented by Karamardian and Schaible.
Abstract: In this paper, several kinds of invariant monotone maps and generalized invariant monotone maps are introduced. Some examples are given which show that invariant monotonicity and generalized invariant monotonicity are proper generalizations of monotonicity and generalized monotonicity. Relationships between generalized invariant monotonicity and generalized invexity are established. Our results are generalizations of those presented by Karamardian and Schaible.

128 citations

Journal ArticleDOI
TL;DR: In this article, a characterization of a preinvex function in terms of its relationship with an intermediate-point pre-vexity and prequasi-inveXity is provided.

124 citations

Journal ArticleDOI
TL;DR: Some new quantum analogues of Hermite-Hadamard and Iyengar type inequalities for some classes of preinvex functions are obtained.

109 citations