https://bura.brunel.ac.uk/bitstream/2438/17829/4/FullText.pdf

Big data analytics in supply chain management: A state-of-the-art literature review

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.

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Convex piecewise-linear fitting

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.

Generalized invexity and generalized invariant monotonicity

On Properties of Preinvex Functions

Some quantum integral inequalities via preinvex functions

Kostreva and Wiecek [3] introduced a problem called LCP-related weighted problem in connection with a multiple objective programming problem, and suggested that a given linear complementarity problem (LCP) can be solved by solving the LCP-related weighted problem associated with it. In this note we provide several clarifications of the claims made in [3]. Finally, we feel that solving any LCP by the approach given in [3] may not be as useful as it is claimed.

A note on linear complementarity problems and multiple objective programming

On singular N0-matrices and the class Q

In this paper we consider a two-person zero-sum discounted stochastic game with ARAT structure and formulate the problem of computing a pair of pure optimal stationary strategies and the corresponding value vector of such a game as a vertical linear complementarity problem. We show that Cottle-Dantzig's algorithm (a generalization of Lemke's algorithm) can solve this problem under a mild assumption.

Vertical linear complementarity and discounted zero-sum stochastic games with ARAT structure

Chapter 1. A Unified Framework for a Class of Mathematical Programming Problems (Dipti Dubey).- Chapter 2. Maximizing spectral radius and number of spanning trees in bipartite graphs (Ravindra B. Bapat).- Chapter 3. Optimization problems on acyclic orientations of graphs, shellability of simplicial complexes, and acyclic partitions (Masahiro Hachimori).- Chapter 4. On ideal minimally non-packing clutters (Kenji Kashiwabara).- Chapter 5. Symmetric Travelling Salesman Problem (Tiru Arthanari).- Chapter 6. About the links between equilibrium problems and variational inequalities (D. Aussel).- Chapter 7. The shrinking projection method and resolvents on Hadamard spaces (Yasunori Kimura).- Chapter 8. Some Hard Stable Marriage Problems: A Survey on Multivariate Analysis (Sushmita Gupta).- Chapter 9. Approximate Quasi-Linearity for Large Incomes (Mamoru Kaneko).- Chapter 10. Cooperative Games in Networks under Uncertainty on the Costs (L. Mallozzi).- Chapter 11. Pricing competition between cell phone carriers in a growing market of customers (Andrey Garnaev).- Chapter 12. Stochastic games with endogenous transitions (Reinoud Joosten).

Mathematical Programming and Game Theory

In this paper we introduce vertical block hidden Z-matrices and study their minimality and complementarity properties.

S. K. Neogy

Papers

A note on linear complementarity problems and multiple objective programming

On singular N0-matrices and the class Q

Vertical linear complementarity and discounted zero-sum stochastic games with ARAT structure

Mathematical Programming and Game Theory

Vertical Block Hidden Z -Matrices And The Generalized Linear Complementarity Problem