4 per page - esslli 2016
... a suitable problem P 0 ⊆ M 0 that is known to be hard for C and a reduction of P 0 to P ...
... a suitable problem P 0 ⊆ M 0 that is known to be hard for C and a reduction of P 0 to P ...
Complexity of Inference in Graphical Models
... joint probability distributions over a large number of variables. Such models are defined as stochastic processes with respect to a graph: each vertex of the graph is associated with a random variable, and the edge structure specifies the conditional independence (Markov) properties among the variab ...
... joint probability distributions over a large number of variables. Such models are defined as stochastic processes with respect to a graph: each vertex of the graph is associated with a random variable, and the edge structure specifies the conditional independence (Markov) properties among the variab ...
On a recursive formulation for solving inverse form finding problems
... In this work we present a recursive method for the determination of the undeformed configuration of a functional component, when only the deformed configuration of a workpiece, the applied forces and the boundary conditions are previously known. This is commonly known as an inverse form finding prob ...
... In this work we present a recursive method for the determination of the undeformed configuration of a functional component, when only the deformed configuration of a workpiece, the applied forces and the boundary conditions are previously known. This is commonly known as an inverse form finding prob ...
On the optimal stopping of a one-dimensional diffusion Damien Lamberton Mihail Zervos
... Such a result is important for the solution to one-dimensional infinite time horizon stochastic control as well as optimal stopping problems using dynamic programming. Indeed, the analysis of several explicitly solvable problems involve such a representation among their assumptions. For constant r , ...
... Such a result is important for the solution to one-dimensional infinite time horizon stochastic control as well as optimal stopping problems using dynamic programming. Indeed, the analysis of several explicitly solvable problems involve such a representation among their assumptions. For constant r , ...
Matt Wolf - CB East Wolf
... Possible Zeros = all fractions that can be created from Step 2 Step 4) Use Descartes’ Rule of Signs to determine the number of positive and negative zeros. # of Positive Zeros = # of sign changes in f (x) or less by an even # # of Negative Zeros = # of sign changes in f ( x) or less by an even # Ba ...
... Possible Zeros = all fractions that can be created from Step 2 Step 4) Use Descartes’ Rule of Signs to determine the number of positive and negative zeros. # of Positive Zeros = # of sign changes in f (x) or less by an even # # of Negative Zeros = # of sign changes in f ( x) or less by an even # Ba ...
Mathematical optimization
In mathematics, computer science and operations research, mathematical optimization (alternatively, optimization or mathematical programming) is the selection of a best element (with regard to some criteria) from some set of available alternatives.In the simplest case, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics. More generally, optimization includes finding ""best available"" values of some objective function given a defined domain (or a set of constraints), including a variety of different types of objective functions and different types of domains.