作者: Coordinate 時間: 2025-3-21 20:39 作者: Cupping 時間: 2025-3-22 03:56
Ivan I. Fishchuk,Andrey Kadashchukwe consider two approaches to the problem of minimizing an arbitrary submodular function: one using the ., and one with a combinatorial algorithm. For the important special case of symmetric submodular functions we mention a simpler algorithm in Section 14.5.作者: 蠟燭 時間: 2025-3-22 06:45
https://doi.org/10.1007/978-1-4939-2547-6mum of the horizontal and the vertical distance. This is often called the ?.-distance. (Older machines can only move either horizontally or vertically at a time; in this case the adjusting time is proportional to the ?.-distance, the sum of the horizontal and the vertical distance.)作者: 抱怨 時間: 2025-3-22 12:08
Helmut Sitter,Claudia Draxl,Michael Ramseyinimum capacity .-cut in both cases; see Sections 12.3 and 12.4. This problem, finding a minimum capacity cut .(.) such that |.∩.| is odd for a specified vertex set ., can be solved with network flow techniques.作者: 自愛 時間: 2025-3-22 14:05 作者: 自愛 時間: 2025-3-22 19:41
,-Matchings and ,-Joins,inimum capacity .-cut in both cases; see Sections 12.3 and 12.4. This problem, finding a minimum capacity cut .(.) such that |.∩.| is odd for a specified vertex set ., can be solved with network flow techniques.作者: Cryptic 時間: 2025-3-23 00:34
0937-5511 supplementary material: .This comprehensive textbook on combinatorial optimization places special emphasis on theoretical results and algorithms with provably good performance, in contrast to heuristics. It has arisen as the basis of several courses on combinatorial optimization and more special top作者: Biomarker 時間: 2025-3-23 01:32
Bin Yi,Kristina Larter,Yaguang Xihe edges correspond to the cables. By Theorem 2.4 the minimal connected spanning subgraphs of a given graph are its spanning trees. So we look for a spanning tree of minimum weight, where we say that a subgraph . of a graph . with weights . : .(.) → ? has weight .(.(.))=∑..(.).作者: 刺耳的聲音 時間: 2025-3-23 07:15
Spanning Trees and Arborescences,he edges correspond to the cables. By Theorem 2.4 the minimal connected spanning subgraphs of a given graph are its spanning trees. So we look for a spanning tree of minimum weight, where we say that a subgraph . of a graph . with weights . : .(.) → ? has weight .(.(.))=∑..(.).作者: 安慰 時間: 2025-3-23 13:38 作者: Truculent 時間: 2025-3-23 17:49 作者: Neonatal 時間: 2025-3-23 20:47
Introduction,s the machine to complete one board as fast as possible. We cannot optimize the drilling time but we can try to minimize the time the machine needs to move from one point to another. Usually drilling machines can move in two directions: the table moves horizontally while the drilling arm moves verti作者: 大廳 時間: 2025-3-23 23:27 作者: 協(xié)議 時間: 2025-3-24 05:13
Minimum Cost Flows,apter 8 one could introduce edge costs to model that the employees have different salaries; our goal is to meet a deadline when all jobs must be finished at a minimum cost. Of course, there are many more applications.作者: 解決 時間: 2025-3-24 08:37
Weighted Matching,extend . to the weighted case and shall again obtain an .(. .)-implementation. This algorithm has many applications, some of which are mentioned in the exercises and in Section 12.2. There are two basic formulations of the weighted matching problem: 作者: 江湖郎中 時間: 2025-3-24 14:40
,-Matchings and ,-Joins,ed as generalizations of the . and also include other important problems. On the other hand, both problems can be reduced to the .. They have combinatorial polynomial-time algorithms as well as polyhedral descriptions. Since in both cases the . turns out to be solvable in polynomial time, we obtain 作者: 減少 時間: 2025-3-24 15:37
Matroids,nd an element of . whose cost is minimum or maximum. In the following we consider modular functions ., i.e. assume that . for all .; equivalently we are given a function .: . → ? and write .(.) = Σ..(.).作者: Isolate 時間: 2025-3-24 20:22 作者: 蛤肉 時間: 2025-3-25 00:04
,-Completeness,are also many important problems for which no polynomial-time algorithm is known. Although we cannot prove that none exists we can show that a polynomial-time algorithm for one “hard” (more precisely: .-hard) problem would imply a polynomialtime algorithm for almost all problems discussed in this bo作者: 可以任性 時間: 2025-3-25 05:54
Multicommodity Flows and Edge-Disjoint Paths,ies), such that the total flow through any edge does not exceed the capacity. We specify the pairs (.) by a second digraph; for technical reasons we have an edge from . to . when we ask for an .-flow. Formally we have: 作者: 侵略 時間: 2025-3-25 09:43
Bernhard Korte,Jens VygenWell-written textbook on combinatorial optimization.One of very few textbooks on this topic.Subject area has manifold applications.Includes supplementary material: 作者: 環(huán)形 時間: 2025-3-25 11:39 作者: GRAZE 時間: 2025-3-25 16:40
https://doi.org/10.1007/978-3-540-71844-4Combinatorial Optimization; Mathematical Programming; algorithms; discrete algorithms; linear optimizati作者: intrigue 時間: 2025-3-25 23:20
978-3-642-09092-9Springer-Verlag Berlin Heidelberg 2008作者: BAIT 時間: 2025-3-26 01:53
Véronique Salone,Mathieu RederstorffGraphs are a fundamental combinatorial structure used throughout this book. In this chapter we not only review the standard definitions and notation, but also prove some basic theorems and mention some fundamental algorithms.作者: GOUGE 時間: 2025-3-26 06:05 作者: Hearten 時間: 2025-3-26 09:12
Northern Blot Detection of Tiny RNAsThere are basically three types of algorithms for .: the . (see Section 3.2), interior point algorithms, and the ..作者: 槍支 時間: 2025-3-26 14:27
Northern Blot Detection of Tiny RNAsIn this chapter, we consider linear programs with integrality constraints: 作者: LUT 時間: 2025-3-26 18:41 作者: myelography 時間: 2025-3-26 23:19
Quentin Thuillier,Isabelle Behm-AnsmantIn this and the next chapter we consider flows in networks. We have a digraph . with edge capacities . : .(.) → ?. and two specified vertices s (the .) and . (the .). The quadruple (.) is sometimes called a ..作者: 不開心 時間: 2025-3-27 02:02 作者: condescend 時間: 2025-3-27 09:18
https://doi.org/10.1007/978-1-4757-6523-6In this chapter we introduce the important concept of approximation algorithms. So far we have dealt mostly with polynomially solvable problems. In the remaining chapters we shall indicate some strategies to cope with .-hard combinatorial optimization problems. Here approximation algorithms must be mentioned in the first place.作者: outset 時間: 2025-3-27 09:27
https://doi.org/10.1007/978-3-642-74913-1The . and the . discussed in earlier chapters are among the “hardest” problems for which a polynomial-time algorithm is known. In this chapter we deal with the following problem which turns out to be, in a sense, the “easiest” .-hard problem: 作者: atopic-rhinitis 時間: 2025-3-27 15:55
Markus Kuhlmann,Blaga Popova,Wolfgang NellenSuppose we have . objects, each of a given size, and some bins of equal capacity. We want to assign the objects to the bins, using as few bins as possible. Of course the total size of the objects assigned to one bin should not exceed its capacity.作者: 粗語 時間: 2025-3-27 17:47 作者: 初次登臺 時間: 2025-3-28 01:57
Linear Programming,In this chapter we review the most important facts about Linear Programming. Although this chapter is self-contained, it cannot be considered to be a comprehensive treatment of the field. The reader unfamiliar with Linear Programming is referred to the textbooks mentioned at the end of this chapter.作者: STING 時間: 2025-3-28 02:05 作者: Visual-Acuity 時間: 2025-3-28 07:10
Integer Programming,In this chapter, we consider linear programs with integrality constraints: 作者: lethal 時間: 2025-3-28 12:49 作者: TIA742 時間: 2025-3-28 15:52 作者: 起來了 時間: 2025-3-28 21:17 作者: 不感興趣 時間: 2025-3-28 23:10
Approximation Algorithms,In this chapter we introduce the important concept of approximation algorithms. So far we have dealt mostly with polynomially solvable problems. In the remaining chapters we shall indicate some strategies to cope with .-hard combinatorial optimization problems. Here approximation algorithms must be mentioned in the first place.作者: 神圣將軍 時間: 2025-3-29 06:02
The Knapsack Problem,The . and the . discussed in earlier chapters are among the “hardest” problems for which a polynomial-time algorithm is known. In this chapter we deal with the following problem which turns out to be, in a sense, the “easiest” .-hard problem: 作者: Gnrh670 時間: 2025-3-29 08:46 作者: BLOT 時間: 2025-3-29 15:27 作者: 極小量 時間: 2025-3-29 15:50 作者: 使激動 時間: 2025-3-29 22:52
Springer Series in Materials Scienceextend . to the weighted case and shall again obtain an .(. .)-implementation. This algorithm has many applications, some of which are mentioned in the exercises and in Section 12.2. There are two basic formulations of the weighted matching problem: 作者: 同來核對 時間: 2025-3-30 03:00 作者: Temporal-Lobe 時間: 2025-3-30 05:44
Small Organic Molecules on Surfacesare also many important problems for which no polynomial-time algorithm is known. Although we cannot prove that none exists we can show that a polynomial-time algorithm for one “hard” (more precisely: .-hard) problem would imply a polynomialtime algorithm for almost all problems discussed in this book (more precisely: all .-easy problems).作者: Substance 時間: 2025-3-30 11:15
Karin van Dijk,Hengping Xu,Heriberto Ceruttiies), such that the total flow through any edge does not exceed the capacity. We specify the pairs (.) by a second digraph; for technical reasons we have an edge from . to . when we ask for an .-flow. Formally we have: 作者: cravat 時間: 2025-3-30 12:40
https://doi.org/10.1007/978-1-4939-2547-6s the machine to complete one board as fast as possible. We cannot optimize the drilling time but we can try to minimize the time the machine needs to move from one point to another. Usually drilling machines can move in two directions: the table moves horizontally while the drilling arm moves verti作者: BRACE 時間: 2025-3-30 17:00
Bin Yi,Kristina Larter,Yaguang Xiuffice to connect all cities and they should be as cheap as possible. It is natural to model the network by a graph: the vertices are the cities and the edges correspond to the cables. By Theorem 2.4 the minimal connected spanning subgraphs of a given graph are its spanning trees. So we look for a s作者: Painstaking 時間: 2025-3-30 22:46
Natural Materials for Organic Electronicsapter 8 one could introduce edge costs to model that the employees have different salaries; our goal is to meet a deadline when all jobs must be finished at a minimum cost. Of course, there are many more applications.作者: 可能性 時間: 2025-3-31 01:31 作者: 確認(rèn) 時間: 2025-3-31 07:38 作者: GUEER 時間: 2025-3-31 12:03
Helmut Sitter,Claudia Draxl,Michael Ramseynd an element of . whose cost is minimum or maximum. In the following we consider modular functions ., i.e. assume that . for all .; equivalently we are given a function .: . → ? and write .(.) = Σ..(.).作者: 暗指 時間: 2025-3-31 15:23
Ivan I. Fishchuk,Andrey Kadashchukiom (M3). In Section 14.1 we consider greedoids, arising by dropping (M2) instead. Moreover, certain polytopes related to matroids and to submodular functions, called polymatroids, lead to strong generalizations of important theorems; we shall discuss them in Section 14.2. In Sections 14.3 and 14.4 作者: 不吉祥的女人 時間: 2025-3-31 17:53
