Combinatorial optimization and theoretical computer science: interfaces and perspectives ; 30th anniversary of the LAMSADE
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
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ISTE
2008
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| Beschreibung: | Includes index. |
| Beschreibung: | 515 S. graph. Darst. |
| ISBN: | 9781848210219 |
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MARC
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| 010 | |a 2007024974 | ||
| 020 | |a 9781848210219 |9 978-1-84821-021-9 | ||
| 035 | |a (OCoLC)144771900 | ||
| 035 | |a (DE-599)BVBBV035115820 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
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| 044 | |a xxu |c US | ||
| 049 | |a DE-703 |a DE-11 | ||
| 050 | 0 | |a QA402.5 | |
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| 084 | |a ST 130 |0 (DE-625)143588: |2 rvk | ||
| 245 | 1 | 0 | |a Combinatorial optimization and theoretical computer science |b interfaces and perspectives ; 30th anniversary of the LAMSADE |c ed. by Vangelis Th. Paschos |
| 264 | 1 | |a London |b ISTE |c 2008 | |
| 264 | 1 | |a Hoboken, NJ |b Wiley | |
| 300 | |a 515 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes index. | ||
| 650 | 4 | |a Informatique - Mathématiques | |
| 650 | 7 | |a Informatique - Mathématiques |2 ram | |
| 650 | 4 | |a Optimisation combinatoire - Logiciels | |
| 650 | 7 | |a Optimisation combinatoire - Logiciels |2 ram | |
| 650 | 4 | |a Informatik | |
| 650 | 4 | |a Mathematik | |
| 650 | 4 | |a Combinatorial optimization |x Computer programs | |
| 650 | 4 | |a Computer science |x Mathematics | |
| 650 | 0 | 7 | |a Kombinatorische Optimierung |0 (DE-588)4031826-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Theoretische Informatik |0 (DE-588)4196735-5 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
| 689 | 0 | 0 | |a Theoretische Informatik |0 (DE-588)4196735-5 |D s |
| 689 | 0 | 1 | |a Kombinatorische Optimierung |0 (DE-588)4031826-6 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Paschos, Vangelis Th. |e Sonstige |4 oth | |
| 856 | 4 | |u http://www.loc.gov/catdir/toc/ecip0720/2007024974.html |3 Table of contents only | |
| 856 | 4 | |u http://www.loc.gov/catdir/enhancements/fy0810/2007024974-b.html |3 Contributor biographical information | |
| 856 | 4 | |u http://www.loc.gov/catdir/enhancements/fy0810/2007024974-d.html |3 Publisher description | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016783559&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016783559 | ||
Datensatz im Suchindex
| _version_ | 1804138090946625536 |
|---|---|
| adam_text | Contents
Preface
.....................................................................................................................15
Chapter
1.
The Complexity of Single Machine Scheduling Problems
under Scenario-based Uncertainty
........................................................................23
Mohamed
Ali ALOULOU
and
Federico
DELLA CROCE
1.1.
Introduction
..................................................................................................23
1.2.
Problem
МтМаАХ^гесЏ^,
θ)...................................................................
25
1.2.1.
Uncertainty on due dates
................................................................25
1.2.2.
Uncertainty on processing times and due dates
.............................27
1.3.
Problem MwMixil||
Σ
wy.C;,wy.)
...............................................................28
1.4.
Problem
МшМгсС
1||
Σ
£/,,#).....................................................................29
1.4.1.
Uncertainty on due dates
................................................................29
1.4.2.
Uncertainty on processing times
....................................................29
1.5.
Bibliography
................................................................................................35
Chapter
2.
Approximation of Multi-criteria
Min
and Max TSP( ,
2)................37
Eric ANGEL, Evripidis BAMPIS, Laurent
GOURVÈS
and Jerome MONNOT
2.1.
Introduction
..................................................................................................37
2.1.1.
The traveling salesman problem
....................................................37
2.1.2.
Multi-criteria optimization
.............................................................38
2.1.3.
Organization of the chapter
............................................................39
2.2.
Overview
......................................................................................................39
2.3.
The bicriteria TSP( ,
2)................................................................................40
2.3.1.
Simple examples of the non-approximability
................................42
2.3.2.
A local search heuristic for the bicriteria TSP{ ,
2).......................43
2.3.3.
A nearest neighbor heuristic for the bicriteria TSP{ ,
2)...............48
2.3.4.
On the bicriteria Max TSP{ ,
2).....................................................52
6
Optimization and Computer Science
2.4.
¿-criteria TSP(l,
2).......................................................................................55
2.4.1.
Non-approximability related to the number of
generated solutions
........................................................................56
2.4.2.
A nearest neighbor heuristic for the ¿-criteria TSP( ,
2)...............60
2.5.
Conclusion
...................................................................................................67
2.6.
Bibliography
................................................................................................68
Chapter
3.
Online Models for Set-covering: The Flaw of Greediness
...............71
Giorgio AUSIELLO, Aristotelis GIANNAKOS and Vangelis Th. PASCHOS
3.1.
Introduction
..................................................................................................71
3.2.
Description of the main results and related work
.........................................73
3.3.
The price of ignorance
.................................................................................76
3.4.
Competitiveness of TAKE-ALL and TAKE-AT-RANDOM
.........................77
3.4.1.
TAKE-ALL algorithm
....................................................................77
3.4.2.
TAKE-AT-RANDOM algorithm
.....................................................78
3.5.
The nasty flaw of greediness
........................................................................79
3.6.
The power of look-ahead
.............................................................................82
3.7.
The maximum budget saving problem
.........................................................88
3.8.
Discussion
....................................................................................................91
3.9.
Bibliography
................................................................................................91
Chapter
4.
Comparison of Expressiveness for Timed Automata and Time
Petri
Nets
.................................................................................................................93
Béatrice BÉRARD, Franck CASSEZ,
Serge HADDAD,
Didier LIME
and
Olivier-Henri ROUX
4.1.
Introduction
..................................................................................................93
4.2.
Time
Petri
nets and timed automata.............................................................
95
4.2.1.
Timed
transition
systems and equivalence relations
......................96
4.2.2.
Time
Petri
nets
...............................................................................98
4.2.3.
Timed automata
...........................................................................101
4.2.4.
Expressiveness and equivalence problems
..................................103
4.3.
Comparison of semantics
/,/1
and PA
........................................................104
4.3.1.
A first comparison between the different semantics of TPNs
......104
4.3.2.
A second comparison for standard bounded TPN
.......................107
4.4.
Strict ordering results
.................................................................................
Ill
4.5.
Equivalence with respect to timed language acceptance
............................113
4.5.1.
Encoding atomic constraints
........................................................113
4.5.2.
Resetting clocks
...........................................................................115
4.5.3.
The complete construction
...........................................................116
4.5.4.
Δ(Α)
and
Λ
accept the same timed language
.............................117
4.5.5.
Consequences of the previous results
..........................................121
Contents 7
4.6. Bisimulation
of
TA
by TPNs
.....................................................................122
4.6.1.
Regions of a timed automaton.....................................................
122
4.6.2.
From bisimulation to uniform bisimulation
.................................124
4.6.3.
A characterization of bisimilarity
................................................128
4.6.4.
Proof of necessity
........................................................................129
4.6.5.
First construction
.........................................................................131
4.6.6.
Second construction
.....................................................................137
4.6.7.
Complexity results
.......................................................................141
4.7.
Conclusion
.................................................................................................142
4.8.
Bibliography
..............................................................................................143
Chapter
5.
A Maximum Node Clustering Problem
.......................................145
Giuliana CARELLO, Federico DELLA CROCE,
Andrea
GROSSO and
Marco LOCATELLI
5.1.
Introduction
................................................................................................149
5.2.
Approximation algorithm for the general problem
....................................147
5.3.
The tree case
..............................................................................................150
5.3.1.
Dynamic programming
................................................................150
5.3.2.
A fully polynomial time approximation scheme
..........................155
5.4.
Exponential algorithms for special cases
...................................................156
5.5.
Bibliography
..............................................................................................159
Chapter
6.
The Patrolling Problem: Theoretical and Experimental Results
..161
Yarn CHEVALEYRE
6.1.
Introduction
................................................................................................161
6.2.
The patrolling task
.....................................................................................162
6.3.
Previous work
............................................................................................164
6.4.
The cyclic strategies
...................................................................................165
6.4.1.
Patrolling with a single-agent
......................................................165
6.4.2.
Extending to multi-agent case
......................................................167
6.4.3.
Optimality of cyclic strategies
.....................................................168
6.5.
Partition-based strategies
...........................................................................169
6.6.
Experiments
...............................................................................................171
6.7.
Conclusion
.................................................................................................172
6.8.
Bibliography
..............................................................................................174
Chapter
7.
Restricted Classes of Utility Functions for Simple Negotiation
Schemes: Sufficiency, Necessity and Maximaliry
...............................................175
Yarm CHEVALEYRE, Ulle ENDRISS and Nicolas MAUDET
7.1.
Introduction
................................................................................................175
7.2.
Myopic negotiation over indivisible resources
..........................................177
7.2.1.
Negotiation problems and deals
...................................................178
8
Optimization and Computer Science
7.2.2.
Negotiating with money
..............................................................179
7.2.3.
Negotiating without money
.........................................................180
7.3.
Convergence for restricted classes of utility functions
..............................180
7.4.
Modular utility functions and variants
.......................................................182
7.5.
Sufficient classes of utility functions
.........................................................184
7.5.1.
Framework with money
...............................................................184
7.5.2.
Framework without money
..........................................................185
7.6.
Necessity issues
.........................................................................................185
7.6.1.
Modularity is not necessary
.........................................................186
7.6.2.
There is no sufficient and necessary class
...................................186
7.6.3.
Evaluating conditions on profiles of utility functions is
intractable
....................................................................................187
7.7.
Maximal classes of utility functions
..........................................................191
7.7.1.
Framework with money
...............................................................192
7.7.2.
Framework without money
..........................................................196
7.8.
Conclusion
.................................................................................................198
7.9.
Bibliography
..............................................................................................199
Chapter
8.
Worst-case Complexity of Exact Algorithms for NP-hard
Problems
................................................................................................................203
Federico
DELLA CROCE,
Bruno ESCOFFIER,
Marcin
KAMIŃSKI
and Vangelis
Th. PASCHOS
8.1.
MAX-CUT
.................................................................................................204
8.1.1.
Extending a partial partition of vertices
.......................................206
8.1.2.
An algorithm for graphs with bounded maximum degree
...........209
8.1.3.
An algorithm for general graphs
..................................................210
8.2.
Pruning the search tree by dominance conditions: the case of
MAXCUT-3
...............................................................................................211
8.2.1.
Dominance conditions
.................................................................212
8.2.2.
The worst-case upper-time bound for MAX-CUT-3
...................214
8.3.
A more careful analysis for pruning: the case of
MIN S-DOMINATING
SET
............................................................................................................226
8.3.1.
Analysis
.......................................................................................227
8.3.2.
Counting
......................................................................................228
8.3.3.
Case n°l
.......................................................................................228
8.3.4.
Case n°2
.......................................................................................232
8.3.5.
Casent
.......................................................................................234
8.3.6.
Complexity analysis
.....................................................................237
8.4.
Bibliography
..............................................................................................239
Contents 9
Chapter
9.
The Online Track Assignment Problem
..........................................241
Marc DEMANGE,
Gabriele
DI STEFANO
and Benjamin
LEROY-BEAULIEU
9.1.
Introduction
................................................................................................241
9.1.1.
Related works
..............................................................................242
9.1.2.
Results
.........................................................................................243
9.2.
Definitions and notations
...........................................................................243
9.3.
Bounds for the permutation graph in a left-to-right model
........................245
9.4.
Bounds for overlap graphs
.........................................................................249
9.5.
Bounds for permutation graphs in a more general model
..........................252
9.6.
Conclusion
.................................................................................................256
9.7.
Bibliography
..............................................................................................256
Chapter
10.
Complexity and Approximation Results for the
Min
Weighted
Node Coloring Problem
........................................................................................259
Marc DEMANGE,
Bruno ESCOFFIER,
Jérôme
MONNOT, Vangelis Th.
PASCHOS and Dominique
DE WERRA
10.1.
Introduction
..............................................................................................259
10.2.
General results
.........................................................................................262
10.2.1.
Structural properties
...................................................................262
10.2.2.
Approximation results
...............................................................264
10.2.2.1.
General graphs
.............................................................264
10.2.2.2.
¿-colorable graphs
.......................................................266
10.2.2.3.
When list coloring is easy
............................................268
10.3.
Weighted node coloring in triangle-free planar graphs
............................270
10.4.
Weighted node coloring in bipartite graphs
.............................................273
10.4.1.
Hardness results
.........................................................................273
10.4.2.
Л-ѓгее
bipartite graphs
..............................................................278
10.5.
Split graphs
..............................................................................................280
10.5.1.
Complexity result
.......................................................................280
10.5.2.
Approximation result
.................................................................281
10.6.
Cographs
..................................................................................................284
10.7.
Interval graphs
.........................................................................................285
10.8.
Bibliography
............................................................................................286
Chapter
11.
Weighted Edge Coloring
.................................................................291
Marc DEMANGE,
Bruno ESCOFFIER, Giorgio LUCARELLI, Ioarmis
MILIS,
Jérôme
MONNOT, Vangelis Th. PASCHOS and Dominique
DE
WERRA
11.1.
Introduction
..............................................................................................291
11.2.
Related problems
.....................................................................................293
11.3.
Preliminaries and notation
.......................................................................294
11.4.
Complexity and (in) approximability
.......................................................295
10
Optimization and
Computer
Science
11.5.
Graphs of
Δ
= 2......................................................................................296
11.6.
A 2-approximation algorithm for general graphs
.....................................298
11.7.
Bipartite graphs
........................................................................................299
11.7.1.
Α ^ζί
-approximation algorithm
..............................................299
11.7.2.
A Z-approximation algorithm for
Δ = 3
...................................301
6
11.7.3.
An approximation algorithm for
Δ
< 7......................................307
11.8.
Trees
.............................................................................................310
11.8.1.
Feasible ^-Coloring and bounded degree trees
..........................310
11.8.2.
Stars of chains
............................................................................312
11.9.
Conclusions
..............................................................................................315
11.10.
Bibliography
..........................................................................................315
Chapter
12.
An Extensive Comparison of
0-1
Linear Programs for
the Daily Satellite Mission Planning
...................................................................319
Virginie GABREL
12.1.
Introduction
..............................................................................................319
12.2.
Different formulations for the daily satellite mission planning
problem
....................................................................................................320
12.2.1.
The daily satellite mission planning problem
............................320
12.2.2.
The natural model
..................................................................320
12.2.3.
The flow formulation
.................................................................321
12.3.
Models comparison
..................................................................................324
12.3.1.
About the stable set polytope
.....................................................324
12.3.2.
Stable set polytope and daily mission planning problem
formulations
...............................................................................325
12.4.
Experiments and results
...........................................................................326
12.5.
Conclusion
...............................................................................................327
12.6.
Bibliography
............................................................................................328
Chapter
13.
Dantzig-Wolfe Decomposition for Linearly Constrained
Stable Set Problem
...............................................................................................329
Virginie
GABREL
13.1.
Introduction
..............................................................................................329
13.2.
The Dantzig-Wolfe decomposition in
0-1
linear programming
...............330
13.3.
The stable set problem with additional linear constraints
........................333
13.4.
Dantzig-Wolfe decomposition on stable set constraints: strengthening
the LP-relaxation
......................................................................................334
13.4.1.
Gap between LP-relaxations of master and initial problems
.....334
13.4.2.
The case of co-comparability graph
...........................................335
13.4.3.
A decomposition scheme for general graphs
.............................337
Contents 11
13.5.
Conclusion
...............................................................................................337
13.6.
Bibliography
............................................................................................338
Chapter
14.
Algorithmic Games
..........................................................................339
Aristotelis GIANNAKOS, Vangelis Th. PASCHOS and Olivier
POTTIÉ
14.1.
Preliminaries
............................................................................................340
14.1.1.
Basic notions on games
..............................................................340
14.1.2.
Complexity classes mentioned throughout the chapter
..............343
14.2.
Nash equilibria
.........................................................................................345
14.3.
Mixed extension of a game and Nash equilibria
......................................347
14.4.
Algorithmic problems
..............................................................................348
14.4.1.
Games of succinct description
...................................................349
14.4.2.
Results related to the computational complexity of a mixed
equilibrium
.................................................................................349
14.4.3.
Counting the number of equilibria in a mixed strategies
game
...........................................................................................355
14.5.
Potential games
........................................................................................355
14.5.1.
Definitions
.................................................................................356
14.5.2.
Properties
...................................................................................356
14.6.
Congestion games
....................................................................................360
14.6.1.
Rosenthal s model
.....................................................................360
14.6.2.
Complexity of congestion games (Rosenthal s model)
.............363
14.6.3.
Other models
..............................................................................364
14.7.
Final note
.................................................................................................368
14.8.
Bibliography
............................................................................................368
Chapter
15.
Flows!
................................................................................................373
Michel KOSKAS and
Cécile MURAT
15.1.
Introduction
..............................................................................................373
15.2.
Definitions and notations
.........................................................................374
15.3.
Presentation of radix trees
........................................................................375
15.3.1.
Database management
...............................................................377
15.3.2.
Image pattern recognition
..........................................................379
15.3.3.
Automatic translation
.................................................................380
15.4.
Shortest path problem
..............................................................................380
15.4.1.
Finding paths in G[t]
..................................................................382
15.4.2.
Complexity
................................................................................385
15.5.
The Flow problem
....................................................................................388
15.5.1.
The Ford-Fulkerson algorithm
...................................................388
15.6.
Conclusion
...............................................................................................390
15.7.
Bibliography
............................................................................................391
12
Optimization and
Computer
Science
Chapter
16.
The Complexity of the Exact Weighted Independent Set
Problem
.................................................................................................................393
Martin
MILANIČ
and Jerome MONNOT
16.1.
Introduction
..............................................................................................393
16.2.
Preliminary observations
..........................................................................397
16.3.
Hardness results
.......................................................................................399
16.3.1.
Bipartite graphs
..........................................................................400
16.3.2.
A more general hardness result
..................................................403
16.4.
Polynomial results
....................................................................................405
16.4.1.
Dynamic programming solutions
...............................................406
16.4.1.1.
тКг-їке
graphs
...........................................................406
16.4.1.2.
Interval graphs
.............................................................407
16.4.1.3.
¿-thin graphs
................................................................408
16.4.1.4.
Circle graphs
................................................................409
16.4.1.5.
Chordal graphs
............................................................410
16.4.1.6.
AT-free graphs
............................................................413
16.4.1.7.
Distance-hereditary graphs
..........................................415
16.4.1.8.
Graphs oftreewidth at most
k
......................................417
16.4.1.9.
Graphs of clique-width at most
k
.................................422
16.4.2.
Modular decomposition
.............................................................424
16.5.
Conclusion
...............................................................................................428
16.6.
Bibliography
............................................................................................429
Chapter
17.
The Labeled Perfect Matching in Bipartite Graphs:
Complexity and (in)Approximability
..................................................................433
Jerome MONNOT
17.1.
Introduction
..............................................................................................433
17.2.
The 2-regular bipartite case
......................................................................436
17.3.
Some
inapproximation
results
..................................................................438
17.4.
The complete bipartite case
......................................................................446
17.5.
Bibliography
............................................................................................452
Chapter
18.
Bounded-size Path Packing Problems
...........................................455
Jerome MONNOT and Sophie TOULOUSE
18.1.
Introduction
..............................................................................................455
18.1.1.
Bounded-size paths packing problems
.......................................455
18.1.2.
Complexity and approximability status
.....................................457
18.1.3.
Theoretical framework, notations and organization
...................458
18.2.
Complexity of P*PARTITION and related problems in bipartite
graphs
.......................................................................................................460
18.2.1.
Negative results from the ¿-dimensional matching problem
.....460
Contents 13
18.2.1.1.
^-dimensional matching problem
................................460
18.2.1.2.
Transforming an instance of
ШМ
into an instance
ofP^PACKING
...........................................................460
18.2.1.3.
Analyzing the obtained instance of Pt PACKING
......462
18.2.1.4.
NP-completeness and APX-hardness
..........................463
18.2.2.
Positive results from the maximum independent set problem
... 466
18.3.
Approximating MAXWP3PACKING and
МІЮ-РАТНРАІШТКЖ...
466
18.3.1.
MAXWPjPACKING in graphs of maximum degree
3.............467
18.3.2.
MAXWPsPACKING in bipartite graphs of maximum
degree
3......................................................................................470
18.3.3.
MIW-PATHPARTITION
in general graphs
............................473
18.4.
Standard and differential approximation of P*P
.......................................475
18.4.1.
Differential approximation of PtP from the traveling salesman
problem
......................................................................................475
18.4.2.
Approximating P4P by means of optimal matchings
.................478
18.4.2.1.
Description of the algorithm
........................................478
18.4.2.2.
General P4P within the standard framework
................479
18.4.2.3.
General P4P within the differential framework
...........482
18.4.2.4.
Bi-valued metric P4P with weights
1
and
2
within
the standard framework
...............................................483
18.4.2.5.
Bi-valued metric P4P with weights a and
b
in the
differential framework
.................................................486
18.5.
Conclusion
...............................................................................................491
18.6.
Bibliography
............................................................................................492
Chapter
19.
An Upper Bound for the Integer Quadratic Multi-knapsack
Problem
.................................................................................................................495
Dominique
QUADRI, Eric SOUTIF,
Pierre
TOLLA
19.1.
Introduction
..............................................................................................495
19.2.
The algorithm of Djerdjour
et al..............................................................
497
19.3.
Improving the upper bound
......................................................................498
19.4.
An efficient heuristic to calculate a feasible solution
...............................500
19.5.
Computational results
..............................................................................500
19.6.
Conclusion
...............................................................................................503
19.7.
Bibliography
............................................................................................503
List of Authors
......................................................................................................507
Index
......................................................................................................................511
|
| adam_txt |
Contents
Preface
.15
Chapter
1.
The Complexity of Single Machine Scheduling Problems
under Scenario-based Uncertainty
.23
Mohamed
Ali ALOULOU
and
Federico
DELLA CROCE
1.1.
Introduction
.23
1.2.
Problem
МтМаАХ^гесЏ^,
θ).
25
1.2.1.
Uncertainty on due dates
.25
1.2.2.
Uncertainty on processing times and due dates
.27
1.3.
Problem MwMixil||
Σ
wy.C;,wy.)
.28
1.4.
Problem
МшМгсС
1||
Σ
£/,,#).29
1.4.1.
Uncertainty on due dates
.29
1.4.2.
Uncertainty on processing times
.29
1.5.
Bibliography
.35
Chapter
2.
Approximation of Multi-criteria
Min
and Max TSP(\,
2).37
Eric ANGEL, Evripidis BAMPIS, Laurent
GOURVÈS
and Jerome MONNOT
2.1.
Introduction
.37
2.1.1.
The traveling salesman problem
.37
2.1.2.
Multi-criteria optimization
.38
2.1.3.
Organization of the chapter
.39
2.2.
Overview
.39
2.3.
The bicriteria TSP(\,
2).40
2.3.1.
Simple examples of the non-approximability
.42
2.3.2.
A local search heuristic for the bicriteria TSP{\,
2).43
2.3.3.
A nearest neighbor heuristic for the bicriteria TSP{\,
2).48
2.3.4.
On the bicriteria Max TSP{\,
2).52
6
Optimization and Computer Science
2.4.
¿-criteria TSP(l,
2).55
2.4.1.
Non-approximability related to the number of
generated solutions
.56
2.4.2.
A nearest neighbor heuristic for the ¿-criteria TSP(\,
2).60
2.5.
Conclusion
.67
2.6.
Bibliography
.68
Chapter
3.
Online Models for Set-covering: The Flaw of Greediness
.71
Giorgio AUSIELLO, Aristotelis GIANNAKOS and Vangelis Th. PASCHOS
3.1.
Introduction
.71
3.2.
Description of the main results and related work
.73
3.3.
The price of ignorance
.76
3.4.
Competitiveness of TAKE-ALL and TAKE-AT-RANDOM
.77
3.4.1.
TAKE-ALL algorithm
.77
3.4.2.
TAKE-AT-RANDOM algorithm
.78
3.5.
The nasty flaw of greediness
.79
3.6.
The power of look-ahead
.82
3.7.
The maximum budget saving problem
.88
3.8.
Discussion
.91
3.9.
Bibliography
.91
Chapter
4.
Comparison of Expressiveness for Timed Automata and Time
Petri
Nets
.93
Béatrice BÉRARD, Franck CASSEZ,
Serge HADDAD,
Didier LIME
and
Olivier-Henri ROUX
4.1.
Introduction
.93
4.2.
Time
Petri
nets and timed automata.
95
4.2.1.
Timed
transition
systems and equivalence relations
.96
4.2.2.
Time
Petri
nets
.98
4.2.3.
Timed automata
.101
4.2.4.
Expressiveness and equivalence problems
.103
4.3.
Comparison of semantics
/,/1
and PA
.104
4.3.1.
A first comparison between the different semantics of TPNs
.104
4.3.2.
A second comparison for standard bounded TPN
.107
4.4.
Strict ordering results
.
Ill
4.5.
Equivalence with respect to timed language acceptance
.113
4.5.1.
Encoding atomic constraints
.113
4.5.2.
Resetting clocks
.115
4.5.3.
The complete construction
.116
4.5.4.
Δ(Α)
and
Λ
accept the same timed language
.117
4.5.5.
Consequences of the previous results
.121
Contents 7
4.6. Bisimulation
of
TA
by TPNs
.122
4.6.1.
Regions of a timed automaton.
122
4.6.2.
From bisimulation to uniform bisimulation
.124
4.6.3.
A characterization of bisimilarity
.128
4.6.4.
Proof of necessity
.129
4.6.5.
First construction
.131
4.6.6.
Second construction
.137
4.6.7.
Complexity results
.141
4.7.
Conclusion
.142
4.8.
Bibliography
.143
Chapter
5.
A "Maximum Node Clustering" Problem
.145
Giuliana CARELLO, Federico DELLA CROCE,
Andrea
GROSSO and
Marco LOCATELLI
5.1.
Introduction
.149
5.2.
Approximation algorithm for the general problem
.147
5.3.
The tree case
.150
5.3.1.
Dynamic programming
.150
5.3.2.
A fully polynomial time approximation scheme
.155
5.4.
Exponential algorithms for special cases
.156
5.5.
Bibliography
.159
Chapter
6.
The Patrolling Problem: Theoretical and Experimental Results
.161
Yarn CHEVALEYRE
6.1.
Introduction
.161
6.2.
The patrolling task
.162
6.3.
Previous work
.164
6.4.
The cyclic strategies
.165
6.4.1.
Patrolling with a single-agent
.165
6.4.2.
Extending to multi-agent case
.167
6.4.3.
Optimality of cyclic strategies
.168
6.5.
Partition-based strategies
.169
6.6.
Experiments
.171
6.7.
Conclusion
.172
6.8.
Bibliography
.174
Chapter
7.
Restricted Classes of Utility Functions for Simple Negotiation
Schemes: Sufficiency, Necessity and Maximaliry
.175
Yarm CHEVALEYRE, Ulle ENDRISS and Nicolas MAUDET
7.1.
Introduction
.175
7.2.
Myopic negotiation over indivisible resources
.177
7.2.1.
Negotiation problems and deals
.178
8
Optimization and Computer Science
7.2.2.
Negotiating with money
.179
7.2.3.
Negotiating without money
.180
7.3.
Convergence for restricted classes of utility functions
.180
7.4.
Modular utility functions and variants
.182
7.5.
Sufficient classes of utility functions
.184
7.5.1.
Framework with money
.184
7.5.2.
Framework without money
.185
7.6.
Necessity issues
.185
7.6.1.
Modularity is not necessary
.186
7.6.2.
There is no sufficient and necessary class
.186
7.6.3.
Evaluating conditions on profiles of utility functions is
intractable
.187
7.7.
Maximal classes of utility functions
.191
7.7.1.
Framework with money
.192
7.7.2.
Framework without money
.196
7.8.
Conclusion
.198
7.9.
Bibliography
.199
Chapter
8.
Worst-case Complexity of Exact Algorithms for NP-hard
Problems
.203
Federico
DELLA CROCE,
Bruno ESCOFFIER,
Marcin
KAMIŃSKI
and Vangelis
Th. PASCHOS
8.1.
MAX-CUT
.204
8.1.1.
Extending a partial partition of vertices
.206
8.1.2.
An algorithm for graphs with bounded maximum degree
.209
8.1.3.
An algorithm for general graphs
.210
8.2.
Pruning the search tree by dominance conditions: the case of
MAXCUT-3
.211
8.2.1.
Dominance conditions
.212
8.2.2.
The worst-case upper-time bound for MAX-CUT-3
.214
8.3.
A more careful analysis for pruning: the case of
MIN S-DOMINATING
SET
.226
8.3.1.
Analysis
.227
8.3.2.
Counting
.228
8.3.3.
Case n°l
.228
8.3.4.
Case n°2
.232
8.3.5.
Casent
.234
8.3.6.
Complexity analysis
.237
8.4.
Bibliography
.239
Contents 9
Chapter
9.
The Online Track Assignment Problem
.241
Marc DEMANGE,
Gabriele
DI STEFANO
and Benjamin
LEROY-BEAULIEU
9.1.
Introduction
.241
9.1.1.
Related works
.242
9.1.2.
Results
.243
9.2.
Definitions and notations
.243
9.3.
Bounds for the permutation graph in a left-to-right model
.245
9.4.
Bounds for overlap graphs
.249
9.5.
Bounds for permutation graphs in a more general model
.252
9.6.
Conclusion
.256
9.7.
Bibliography
.256
Chapter
10.
Complexity and Approximation Results for the
Min
Weighted
Node Coloring Problem
.259
Marc DEMANGE,
Bruno ESCOFFIER,
Jérôme
MONNOT, Vangelis Th.
PASCHOS and Dominique
DE WERRA
10.1.
Introduction
.259
10.2.
General results
.262
10.2.1.
Structural properties
.262
10.2.2.
Approximation results
.264
10.2.2.1.
General graphs
.264
10.2.2.2.
¿-colorable graphs
.266
10.2.2.3.
When list coloring is easy
.268
10.3.
Weighted node coloring in triangle-free planar graphs
.270
10.4.
Weighted node coloring in bipartite graphs
.273
10.4.1.
Hardness results
.273
10.4.2.
Л-ѓгее
bipartite graphs
.278
10.5.
Split graphs
.280
10.5.1.
Complexity result
.280
10.5.2.
Approximation result
.281
10.6.
Cographs
.284
10.7.
Interval graphs
.285
10.8.
Bibliography
.286
Chapter
11.
Weighted Edge Coloring
.291
Marc DEMANGE,
Bruno ESCOFFIER, Giorgio LUCARELLI, Ioarmis
MILIS,
Jérôme
MONNOT, Vangelis Th. PASCHOS and Dominique
DE
WERRA
11.1.
Introduction
.291
11.2.
Related problems
.293
11.3.
Preliminaries and notation
.294
11.4.
Complexity and (in) approximability
.295
10
Optimization and
Computer
Science
11.5.
Graphs of
Δ
= 2.296
11.6.
A 2-approximation algorithm for general graphs
.298
11.7.
Bipartite graphs
.299
11.7.1.
Α ^ζί
-approximation algorithm
.299
11.7.2.
A Z-approximation algorithm for
Δ = 3
.301
6
11.7.3.
An approximation algorithm for
Δ
< 7.307
11.8.
Trees
.310
11.8.1.
Feasible ^-Coloring and bounded degree trees
.310
11.8.2.
Stars of chains
.312
11.9.
Conclusions
.315
11.10.
Bibliography
.315
Chapter
12.
An Extensive Comparison of
0-1
Linear Programs for
the Daily Satellite Mission Planning
.319
Virginie GABREL
12.1.
Introduction
.319
12.2.
Different formulations for the daily satellite mission planning
problem
.320
12.2.1.
The daily satellite mission planning problem
.320
12.2.2.
The "natural" model
.320
12.2.3.
The flow formulation
.321
12.3.
Models comparison
.324
12.3.1.
About the stable set polytope
.324
12.3.2.
Stable set polytope and daily mission planning problem
formulations
.325
12.4.
Experiments and results
.326
12.5.
Conclusion
.327
12.6.
Bibliography
.328
Chapter
13.
Dantzig-Wolfe Decomposition for Linearly Constrained
Stable Set Problem
.329
Virginie
GABREL
13.1.
Introduction
.329
13.2.
The Dantzig-Wolfe decomposition in
0-1
linear programming
.330
13.3.
The stable set problem with additional linear constraints
.333
13.4.
Dantzig-Wolfe decomposition on stable set constraints: strengthening
the LP-relaxation
.334
13.4.1.
Gap between LP-relaxations of master and initial problems
.334
13.4.2.
The case of co-comparability graph
.335
13.4.3.
A decomposition scheme for general graphs
.337
Contents 11
13.5.
Conclusion
.337
13.6.
Bibliography
.338
Chapter
14.
Algorithmic Games
.339
Aristotelis GIANNAKOS, Vangelis Th. PASCHOS and Olivier
POTTIÉ
14.1.
Preliminaries
.340
14.1.1.
Basic notions on games
.340
14.1.2.
Complexity classes mentioned throughout the chapter
.343
14.2.
Nash equilibria
.345
14.3.
Mixed extension of a game and Nash equilibria
.347
14.4.
Algorithmic problems
.348
14.4.1.
Games of succinct description
.349
14.4.2.
Results related to the computational complexity of a mixed
equilibrium
.349
14.4.3.
Counting the number of equilibria in a mixed strategies
game
.355
14.5.
Potential games
.355
14.5.1.
Definitions
.356
14.5.2.
Properties
.356
14.6.
Congestion games
.360
14.6.1.
Rosenthal's model
.360
14.6.2.
Complexity of congestion games (Rosenthal's model)
.363
14.6.3.
Other models
.364
14.7.
Final note
.368
14.8.
Bibliography
.368
Chapter
15.
Flows!
.373
Michel KOSKAS and
Cécile MURAT
15.1.
Introduction
.373
15.2.
Definitions and notations
.374
15.3.
Presentation of radix trees
.375
15.3.1.
Database management
.377
15.3.2.
Image pattern recognition
.379
15.3.3.
Automatic translation
.380
15.4.
Shortest path problem
.380
15.4.1.
Finding paths in G[t]
.382
15.4.2.
Complexity
.385
15.5.
The Flow problem
.388
15.5.1.
The Ford-Fulkerson algorithm
.388
15.6.
Conclusion
.390
15.7.
Bibliography
.391
12
Optimization and
Computer
Science
Chapter
16.
The Complexity of the Exact Weighted Independent Set
Problem
.393
Martin
MILANIČ
and Jerome MONNOT
16.1.
Introduction
.393
16.2.
Preliminary observations
.397
16.3.
Hardness results
.399
16.3.1.
Bipartite graphs
.400
16.3.2.
A more general hardness result
.403
16.4.
Polynomial results
.405
16.4.1.
Dynamic programming solutions
.406
16.4.1.1.
тКг-їке
graphs
.406
16.4.1.2.
Interval graphs
.407
16.4.1.3.
¿-thin graphs
.408
16.4.1.4.
Circle graphs
.409
16.4.1.5.
Chordal graphs
.410
16.4.1.6.
AT-free graphs
.413
16.4.1.7.
Distance-hereditary graphs
.415
16.4.1.8.
Graphs oftreewidth at most
k
.417
16.4.1.9.
Graphs of clique-width at most
k
.422
16.4.2.
Modular decomposition
.424
16.5.
Conclusion
.428
16.6.
Bibliography
.429
Chapter
17.
The Labeled Perfect Matching in Bipartite Graphs:
Complexity and (in)Approximability
.433
Jerome MONNOT
17.1.
Introduction
.433
17.2.
The 2-regular bipartite case
.436
17.3.
Some
inapproximation
results
.438
17.4.
The complete bipartite case
.446
17.5.
Bibliography
.452
Chapter
18.
Bounded-size Path Packing Problems
.455
Jerome MONNOT and Sophie TOULOUSE
18.1.
Introduction
.455
18.1.1.
Bounded-size paths packing problems
.455
18.1.2.
Complexity and approximability status
.457
18.1.3.
Theoretical framework, notations and organization
.458
18.2.
Complexity of P*PARTITION and related problems in bipartite
graphs
.460
18.2.1.
Negative results from the ¿-dimensional matching problem
.460
Contents 13
18.2.1.1.
^-dimensional matching problem
.460
18.2.1.2.
Transforming an instance of
ШМ
into an instance
ofP^PACKING
.460
18.2.1.3.
Analyzing the obtained instance of Pt PACKING
.462
18.2.1.4.
NP-completeness and APX-hardness
.463
18.2.2.
Positive results from the maximum independent set problem
. 466
18.3.
Approximating MAXWP3PACKING and
МІЮ-РАТНРАІШТКЖ.
466
18.3.1.
MAXWPjPACKING in graphs of maximum degree
3.467
18.3.2.
MAXWPsPACKING in bipartite graphs of maximum
degree
3.470
18.3.3.
MIW-PATHPARTITION
in general graphs
.473
18.4.
Standard and differential approximation of P*P
.475
18.4.1.
Differential approximation of PtP from the traveling salesman
problem
.475
18.4.2.
Approximating P4P by means of optimal matchings
.478
18.4.2.1.
Description of the algorithm
.478
18.4.2.2.
General P4P within the standard framework
.479
18.4.2.3.
General P4P within the differential framework
.482
18.4.2.4.
Bi-valued metric P4P with weights
1
and
2
within
the standard framework
.483
18.4.2.5.
Bi-valued metric P4P with weights a and
b
in the
differential framework
.486
18.5.
Conclusion
.491
18.6.
Bibliography
.492
Chapter
19.
An Upper Bound for the Integer Quadratic Multi-knapsack
Problem
.495
Dominique
QUADRI, Eric SOUTIF,
Pierre
TOLLA
19.1.
Introduction
.495
19.2.
The algorithm of Djerdjour
et al.
497
19.3.
Improving the upper bound
.498
19.4.
An efficient heuristic to calculate a feasible solution
.500
19.5.
Computational results
.500
19.6.
Conclusion
.503
19.7.
Bibliography
.503
List of Authors
.507
Index
.511 |
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| any_adam_object_boolean | 1 |
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| dewey-sort | 3519.6 14 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik |
| discipline_str_mv | Informatik Mathematik |
| format | Book |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV035115820 |
| illustrated | Illustrated |
| index_date | 2024-07-02T22:19:33Z |
| indexdate | 2024-07-09T21:22:40Z |
| institution | BVB |
| isbn | 9781848210219 |
| language | English |
| lccn | 2007024974 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016783559 |
| oclc_num | 144771900 |
| open_access_boolean | |
| owner | DE-703 DE-11 |
| owner_facet | DE-703 DE-11 |
| physical | 515 S. graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | ISTE Wiley |
| record_format | marc |
| spelling | Combinatorial optimization and theoretical computer science interfaces and perspectives ; 30th anniversary of the LAMSADE ed. by Vangelis Th. Paschos London ISTE 2008 Hoboken, NJ Wiley 515 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes index. Informatique - Mathématiques Informatique - Mathématiques ram Optimisation combinatoire - Logiciels Optimisation combinatoire - Logiciels ram Informatik Mathematik Combinatorial optimization Computer programs Computer science Mathematics Kombinatorische Optimierung (DE-588)4031826-6 gnd rswk-swf Theoretische Informatik (DE-588)4196735-5 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Theoretische Informatik (DE-588)4196735-5 s Kombinatorische Optimierung (DE-588)4031826-6 s DE-604 Paschos, Vangelis Th. Sonstige oth http://www.loc.gov/catdir/toc/ecip0720/2007024974.html Table of contents only http://www.loc.gov/catdir/enhancements/fy0810/2007024974-b.html Contributor biographical information http://www.loc.gov/catdir/enhancements/fy0810/2007024974-d.html Publisher description Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016783559&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Combinatorial optimization and theoretical computer science interfaces and perspectives ; 30th anniversary of the LAMSADE Informatique - Mathématiques Informatique - Mathématiques ram Optimisation combinatoire - Logiciels Optimisation combinatoire - Logiciels ram Informatik Mathematik Combinatorial optimization Computer programs Computer science Mathematics Kombinatorische Optimierung (DE-588)4031826-6 gnd Theoretische Informatik (DE-588)4196735-5 gnd |
| subject_GND | (DE-588)4031826-6 (DE-588)4196735-5 (DE-588)4143413-4 |
| title | Combinatorial optimization and theoretical computer science interfaces and perspectives ; 30th anniversary of the LAMSADE |
| title_auth | Combinatorial optimization and theoretical computer science interfaces and perspectives ; 30th anniversary of the LAMSADE |
| title_exact_search | Combinatorial optimization and theoretical computer science interfaces and perspectives ; 30th anniversary of the LAMSADE |
| title_exact_search_txtP | Combinatorial optimization and theoretical computer science interfaces and perspectives ; 30th anniversary of the LAMSADE |
| title_full | Combinatorial optimization and theoretical computer science interfaces and perspectives ; 30th anniversary of the LAMSADE ed. by Vangelis Th. Paschos |
| title_fullStr | Combinatorial optimization and theoretical computer science interfaces and perspectives ; 30th anniversary of the LAMSADE ed. by Vangelis Th. Paschos |
| title_full_unstemmed | Combinatorial optimization and theoretical computer science interfaces and perspectives ; 30th anniversary of the LAMSADE ed. by Vangelis Th. Paschos |
| title_short | Combinatorial optimization and theoretical computer science |
| title_sort | combinatorial optimization and theoretical computer science interfaces and perspectives 30th anniversary of the lamsade |
| title_sub | interfaces and perspectives ; 30th anniversary of the LAMSADE |
| topic | Informatique - Mathématiques Informatique - Mathématiques ram Optimisation combinatoire - Logiciels Optimisation combinatoire - Logiciels ram Informatik Mathematik Combinatorial optimization Computer programs Computer science Mathematics Kombinatorische Optimierung (DE-588)4031826-6 gnd Theoretische Informatik (DE-588)4196735-5 gnd |
| topic_facet | Informatique - Mathématiques Optimisation combinatoire - Logiciels Informatik Mathematik Combinatorial optimization Computer programs Computer science Mathematics Kombinatorische Optimierung Theoretische Informatik Aufsatzsammlung |
| url | http://www.loc.gov/catdir/toc/ecip0720/2007024974.html http://www.loc.gov/catdir/enhancements/fy0810/2007024974-b.html http://www.loc.gov/catdir/enhancements/fy0810/2007024974-d.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016783559&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT paschosvangelisth combinatorialoptimizationandtheoreticalcomputerscienceinterfacesandperspectives30thanniversaryofthelamsade |