Extension of stochastic dominance theory to random variables
Extension of stochastic dominance theory to random variables
In this paper, we develop some stochastic dominance theorems for the location and scale family and linear combinations of random variables and for risk lovers as well as risk averters that extend results in Hadar and Russell (1971) and Tesfatsion (1976). The results are discussed and applied to decision-making.
Extension principle and fuzzy-mathematical programming
Extensions of convex functionals on convex cones
We prove that under some topological assumptions (e.g. if M has nonempty interior in X), a convex cone M in a linear topological space X is a linear subspace if and only if each convex functional on M has a convex extension on the whole space X.
Extensions of cyclically monotone mappings
Extensions of Foster's averaging formula to infinite networks with moderate growth.
Extensions of pseudo-boolean programming
Extra multistep BFGS updates in quasi-Newton methods.
Extrema constrained by a family of curves.
Extrema constrained by curves.
Extrema with constraints on points and/or velocities.
Extremal moment methods and stochastic orders.
Extremal moment methods and stochastic orders. Application in actuarial science.
Extreme point enumeration technique for assignment problem
Extreme points of a convex polytope and extreme rays of the corresponding convex cone
Extremum conditions for nonlinear functions on convex sets
Extremum-searching hierarchical parallel probabilistic algorithms