Titlebook: Operations Research Proceedings 1996; Selected Papers of t Uwe Zimmermann,Ulrich Derigs,Karl-Peter Schuster Conference proceedings 1997 Spr

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書目名稱Operations Research Proceedings 1996影響因子(影響力)




書目名稱Operations Research Proceedings 1996影響因子(影響力)學(xué)科排名




書目名稱Operations Research Proceedings 1996網(wǎng)絡(luò)公開度




書目名稱Operations Research Proceedings 1996網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Operations Research Proceedings 1996被引頻次




書目名稱Operations Research Proceedings 1996被引頻次學(xué)科排名




書目名稱Operations Research Proceedings 1996年度引用




書目名稱Operations Research Proceedings 1996年度引用學(xué)科排名




書目名稱Operations Research Proceedings 1996讀者反饋




書目名稱Operations Research Proceedings 1996讀者反饋學(xué)科排名

Brain-Waves

的闡明

A Flexible Approach to Piecewise Linear Multiple Objective Programmingrgestellt. Der Ansatz beruht auf einer Unterteilung des Raumes in Zellen, in denen das Problem linear ist. Die Konzepte der Zell-Effizienz sowie der Komplex-Effizienz und ihre Beziehung zur allgemeinen Effizienz werden dargestellt. Anwendungen in Standorttheorie und Worst-Case Analyse werden besproc

反復(fù)拉緊

Analysis of regularization techniques in convex nondifferentiable optimizationon. We show that they share a common motivation in terms of two-objective programming and that in fact they are aimed to solve parametrically such a program..In particular we focus on the Moreau-Yosida regularization of the cutting plane function and discuss the trajectory of its optimal point with

無辜

A Bregman-Projected Subgradient Method for Convex Constrained Nondifferentiable Minimizationecause it is simple and easy to implement. We generalize this algorithm by allowing inexact Bregman’s nonorthogonal projections. They stem from Bregman distances generated by “sufficiently well-behaved” strictly convex functions, whose domains coincide with the feasible set. Inexact Bregman projecti

談判

colony

Robust Optimisation of Nonlinear Systems under Parametric Uncertaintyr the mean-variance robustness framework for uncer-tainty characterised by continuous probability density functions. A robust formulation designed for . feasibility over the entire probability space is presented. Flexibility in the operational conditions is also provided via a penalty function frame

刺激

容易懂得

On the Average Behaviour of Primal and Dual Greedy Algorithms for the Knapsack Problemms, the worst-case behaviour is well-known (cf. [2]), for the dual greedy algorithm it can be arbitrarily bad. We study the average performance of both algorithms. It is shown (on the basis of the Central Limit Theorem) that in average the objective function value of the dual greedy algorithm differ

dermatomyositis

Packing a Bin Online to Maximize the Total Number of Itemshe future. An online algorithm A must irrevocably decide whether or not to put an item into the bin whenever it is presented. The goal is to maximize the number of items collected. A is .-competitive for some function . if .*(.) ≤ .(..(.)) holds for all sequences ., where .* is the (theoretical) opt