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Linear Operators Preserving Column Majorization of the (0, 1)-Vectors
The paper provides a characterization of linear operators preserving column majorization of the (0, 1)-vectors. In addition, such operators are...
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Convex decompositions of Q-stochastic tensors and Bell locality in a multipartite system
Generalizing the notions of the row and the column stochastic matrices, we introduce the multidimensional Q -stochastic tensors. We prove that every Q -...
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Enhanced alternating energy minimization methods for stochastic galerkin matrix equations
In uncertainty quantification, it is commonly required to solve a forward model consisting of a partial differential equation (PDE) with a spatially...
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Time-inconsistent stochastic linear-quadratic control problem with indefinite control weight costs
A time-inconsistent linear-quadratic optimal control problem for stochastic differential equations is studied. We introduce conditions where the...
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Casimir preserving stochastic Lie–Poisson integrators
Casimir preserving integrators for stochastic Lie–Poisson equations with Stratonovich noise are developed, extending Runge–Kutta Munthe-Kaas methods....
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Krause Mean Processes Generated by Cubic Stochastic Matrices with Weak Influences
AbstractHistorically, the concept of consensus formation through iterative averaging was initially propounded by M. H. DeGroot. Subsequently, it has...
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Stochastic Programming Models
In this chapter, we present formulations of stochastic programs involving risk aversion and describe their properties. Risk aversion is very...
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Stochastic Toolkits
This chapter introduces the basic probability concepts and stochastic processes. It includes random variables, probability density function (PDF),...
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Data-Driven Direct Adaptive Risk-Sensitive Control of Stochastic Systems
The authors propose a data-driven direct adaptive control law based on the adaptive dynamic programming (ADP) algorithm for continuous-time...
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Example Applications of Stochastic Programming
In this chapter, we preview a variety of example applications of stochastic programming (SP). These applications include flexible manufacturing...
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Block Mirror Stochastic Gradient Method For Stochastic Optimization
In this paper, a block mirror stochastic gradient method is developed to solve stochastic optimization problems involving convex and nonconvex cases,...
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Optimization with Stochastic Dominance Constraints
Preference between random variables is frequently expressed by comparing their distributions via a stochastic order. A variety of stochastic-order...
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A block-randomized stochastic method with importance sampling for CP tensor decomposition
One popular way to compute the CANDECOMP/PARAFAC (CP) decomposition of a tensor is to transform the problem into a sequence of overdetermined least...
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Applications of Quadratic Stochastic Operators to Nonlinear Consensus Problems
Historically, an idea of reaching consensus through repeated averaging was introduced by DeGroot for a structured time-invariant and synchronous...
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Ergodicity Index of a Set of Stochastic Matrices
The paper introduces and explores the notions of ergodicity index and ergodicity exponent of a set of stochastic matrices. For the ergodicity...
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Stochastic Simulation Algorithms for Iterative Solution of the Lamé Equation
AbstractIn this paper, iterative stochastic simulation algorithms for the Lamé equation describing the displacements of an isotropic elastic body are...
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Stochastic Stability of Discrete Time Positive Markov Jump Nonlinear Systems
This paper investigates the stochastic stability of discrete time positive Markov jump nonlinear systems (PMJNS). Some definitions on stochastic...