S
S. K. Neogy
Researcher at Indian Statistical Institute
Publications - 44
Citations - 865
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
On Invex Sets and Preinvex Functions
S. R. Mohan,S. K. Neogy +1 more
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.
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The generalized linear complementarity problem revisited
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.
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More on positive subdefinite matrices and the linear complementarity problem
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.
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On the classes of fully copositive and fully semimonotone matrices
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.
BookDOI
Mathematical programming and game theory for decision making
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.