TL;DR: In this paper, Doubly Stochastic Matrices and Schur-Convex Functions are used to represent matrix functions in the context of matrix factorizations, compounds, direct products and M-matrices.
Abstract: Introduction.- Doubly Stochastic Matrices.- Schur-Convex Functions.- Equivalent Conditions for Majorization.- Preservation and Generation of Majorization.- Rearrangements and Majorization.- Combinatorial Analysis.- Geometric Inequalities.- Matrix Theory.- Numerical Analysis.- Stochastic Majorizations.- Probabilistic, Statistical, and Other Applications.- Additional Statistical Applications.- Orderings Extending Majorization.- Multivariate Majorization.- Convex Functions and Some Classical Inequalities.- Stochastic Ordering.- Total Positivity.- Matrix Factorizations, Compounds, Direct Products, and M-Matrices.- Extremal Representations of Matrix Functions.
TL;DR: In this paper, the authors present general results on Positive and Negative Definite Matrices and Kernels on Abelian Semigroups and define simple properties of positive and negative Definite Kernels.
Abstract: 1 Introduction to Locally Convex Topological Vector Spaces and Dual Pairs- 1 Locally Convex Vector Spaces- 2 Hahn-Banach Theorems- 3 Dual Pairs- Notes and Remarks- 2 Radon Measures and Integral Representations- 1 Introduction to Radon Measures on Hausdorff Spaces- 2 The Riesz Representation Theorem- 3 Weak Convergence of Finite Radon Measures- 4 Vague Convergence of Radon Measures on Locally Compact Spaces- 5 Introduction to the Theory of Integral Representations- Notes and Remarks- 3 General Results on Positive and Negative Definite Matrices and Kernels- 1 Definitions and Some Simple Properties of Positive and Negative Definite Kernels- 2 Relations Between Positive and Negative Definite Kernels- 3 Hubert Space Representation of Positive and Negative Definite Kernels- Notes and Remarks- 4 Main Results on Positive and Negative Definite Functions on Semigroups- 1 Definitions and Simple Properties 86 2 Exponentially Bounded Positive Definite Functions on Abelian Semigroups- 3 Negative Definite Functions on Abelian Semigroups- 4 Examples of Positive and Negative Definite Functions- 5 T-Positive Functions- 6 Completely Monotone and Alternating Functions- Notes and Remarks- 5 Schoenberg-Type Results for Positive and Negative Definite Functions- 1 Schoenberg Triples- 2 Norm Dependent Positive Definite Functions on Banach Spaces- 3 Functions Operating on Positive Definite Matrices- 4 Schoenberg's Theorem for the Complex Hilbert Sphere- 5 The Real Infinite Dimensional Hyperbolic Space- Notes and Remarks- 6 Positive Definite Functions and Moment Functions- 1 Moment Functions- 2 The One-Dimensional Moment Problem- 3 The Multi-Dimensional Moment Problem- 4 The Two-Sided Moment Problem- 5 Perfect Semigroups- Notes and Remarks- 7 Hoeffding's Inequality and Multivariate Majorization- 1 The Discrete Case- 2 Extension to Nondiscrete Semigroups- 3 Completely Negative Definite Functions and Schur-Monotonicity- Notes and Remarks- 8 Positive and Negative Definite Functions on Abelian Semigroups Without Zero- 1 Quasibounded Positive and Negative Definite Functions- 2 Completely Monotone and Completely Alternating Functions- Notes and Remarks- References- List of Symbols
TL;DR: In this article, the authors introduce the notion of Doubly Stochastic Matrices (DSM) and compare its properties with those of other matrices with minimum permanent and double sub-and superstochasticity.
TL;DR: In this paper, the authors present a functional calculus and derivation of matrix monotone functions and convexity of matrix means and inequalities, majorization and singular values, and some applications.
Abstract: Fundamentals of operators and matrices.- Mappings and algebras.- Functional calculus and derivation.- Matrix monotone functions and convexity.- Matrix means and inequalities.- Majorization and singular values.- Some applications.
TL;DR: The best previously known results for the multiple-choice processes in the heavily loaded case were obtained using majorization by the single-choice process, so this paper yields an upper bound of the maximum load of bins of $m/n + {\mbox{$\cal O$}}(\sqrt{m \ln n \,/\, n})$ with high probability.
Abstract: We investigate balls-into-bins processes allocating m balls into n bins based on the multiple-choice paradigm. In the classical single-choice variant each ball is placed into a bin selected uniformly at random. In a multiple-choice process each ball can be placed into one out of $d \ge 2$ randomly selected bins. It is known that in many scenarios having more than one choice for each ball can improve the load balance significantly. Formal analyses of this phenomenon prior to this work considered mostly the lightly loaded case, that is, when $m \approx n$. In this paper we present the first tight analysis in the heavily loaded case, that is, when $m \gg n$ rather than $m \approx n$.The best previously known results for the multiple-choice processes in the heavily loaded case were obtained using majorization by the single-choice process. This yields an upper bound of the maximum load of bins of $m/n + {\mbox{$\cal O$}}(\sqrt{m \ln n \,/\, n})$ with high probability. We show, however, that the multiple-choice...