Titlebook: Mathematical Programming at Oberwolfach II; Bernhard Korte,Klaus Ritter Book 1984Latest edition Springer-Verlag Berlin Heidelberg 1984 Mat

擔(dān)心

Inaccuracy in quasi-Newton methods: Local improvement theorems,re only inaccurate .-values are available. Our analysis is for the case where the relative error in . is less than one. We obtain theorems specifying local rates of improvement and limiting accuracies depending on the nearness to Newton’s method of the basic algorithm, the accuracy of its implementa

詞根詞綴法

Orientation of matrices,ix. In this paper we describe a finite algorithm to make this transformation, based on the results of Camion. Our main result is that when . is a totally unimodular matrix this transformation can be made by a polynomial algorithm.

COLON

Covering directed and odd cuts,n directed cuts and Seymour’s theorem on odd cuts. Here we strengthen these results (incidently providing a simple proof of Seymour’s theorem). For example, the minimum cardinality of a .-join in a graph .=(.) is proved to equal the maximum of Σ..(..)/2 over all partitions of . where ..(.) is the nu

epinephrine

骯臟

Inner estimates for the generalized gradient of the optimal value function in nonlinear programmingertain assumptions there exists an optimal solution of the reference problem whose Lagrange multiplier set intersects the generalized gradient of . at .. We investigate such relationships in more detail and prove that this generalized gradient must meet any compact multiplier set (which is relevant

冒煙

培養(yǎng)

Modelling with integer variables,xtend earlier ones due to R.R. Meyer. We provide representations whose linear relaxations have an efficient formulation, while retaining maximum accuracy to the problem modelled. Under mild hypotheses, these properties are retained as integer variables are set to values in nodes of branch-and-bound

Germinate

Multipoint methods for separable nonlinear networks, grid’ approximations of 2, 4, and 6 segments per variable and ‘implicit grid’ strategies that generate segments as needed, but store at most a 2-segment approximation at any time. It is shown that the implicit grid methods are linearly convergent, and this predicted behavior is confirmed by highly

Misgiving

Normal solutions of linear programs, positive multiples of the gradient of the objective function. This projection turns out to be the normal solution obtained by projecting the origin on the optimal solution set. By seeking the solution with least 2-norm which minimizes the l-norm infeasibility measure of a system of linear inequalit

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