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Combinatorial optimization [[electronic resource] ] . Volume 2 Paradigms of combinatorial optimization : problems and new approaches / / edited by Vangelis Th. Paschos
Combinatorial optimization [[electronic resource] ] . Volume 2 Paradigms of combinatorial optimization : problems and new approaches / / edited by Vangelis Th. Paschos
Pubbl/distr/stampa London, : ISTE, 2010
Descrizione fisica 1 online resource (722 p.)
Disciplina 519.64
Altri autori (Persone) PaschosVangelis Th
Collana ISTE
Soggetto topico Combinatorial optimization
Programming (Mathematics)
ISBN 1-118-60020-7
1-118-60027-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto pt. I. Paradigmatic problems -- pt. II. New approaches.
Record Nr. UNISA-996212040103316
London, : ISTE, 2010
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Combinatorial optimization [[electronic resource] ] . Volume 2 Paradigms of combinatorial optimization : problems and new approaches / / edited by Vangelis Th. Paschos
Combinatorial optimization [[electronic resource] ] . Volume 2 Paradigms of combinatorial optimization : problems and new approaches / / edited by Vangelis Th. Paschos
Pubbl/distr/stampa London, : ISTE, 2010
Descrizione fisica 1 online resource (722 p.)
Disciplina 519.64
Altri autori (Persone) PaschosVangelis Th
Collana ISTE
Soggetto topico Combinatorial optimization
Programming (Mathematics)
ISBN 1-118-60020-7
1-118-60027-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto pt. I. Paradigmatic problems -- pt. II. New approaches.
Record Nr. UNINA-9910830731903321
London, : ISTE, 2010
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Combinatorial optimization [[electronic resource] ] . Volume 2 Paradigms of combinatorial optimization : problems and new approaches / / edited by Vangelis Th. Paschos
Combinatorial optimization [[electronic resource] ] . Volume 2 Paradigms of combinatorial optimization : problems and new approaches / / edited by Vangelis Th. Paschos
Pubbl/distr/stampa London, : ISTE, 2010
Descrizione fisica 1 online resource (722 p.)
Disciplina 519.64
Altri autori (Persone) PaschosVangelis Th
Collana ISTE
Soggetto topico Combinatorial optimization
Programming (Mathematics)
ISBN 1-118-60020-7
1-118-60027-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto pt. I. Paradigmatic problems -- pt. II. New approaches.
Record Nr. UNINA-9910841053703321
London, : ISTE, 2010
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Combinatorial optimization and theoretical computer science [[electronic resource] ] : interfaces and perspectives : 30th anniversary of the LAMSADE / / edited by Vangelis Th. Paschos
Combinatorial optimization and theoretical computer science [[electronic resource] ] : interfaces and perspectives : 30th anniversary of the LAMSADE / / edited by Vangelis Th. Paschos
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (518 p.)
Disciplina 519.6/4
519.64
Altri autori (Persone) PaschosVangelis Th
Collana ISTE
Soggetto topico Combinatorial optimization - Computer programs
Computer science - Mathematics
Soggetto genere / forma Electronic books.
ISBN 1-282-16499-6
9786612164996
0-470-61109-X
0-470-39367-X
Classificazione SK 890
ST 130
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Combinatorial Optimization and Theoretical Computer Science; Contents; Preface; Chapter 1. The Complexity of Single Machine Scheduling Problems under Scenario-based Uncertainty; 1.1. Introduction; 1.2. Problem MinMax(1|prec|fmax, θ ); 1.2.1. Uncertainty on due dates; 1.2.2. Uncertainty on processing times and due dates; 1.3. Problem MinMax(1|| Σ wj Cj, Wj ); 1.4. Problem MinMax(1|| Σ Uj, θ ); 1.4.1. Uncertainty on due dates; 1.4.2. Uncertainty on processing times; 1.5. Bibliography; Chapter 2. Approximation of Multi-criteria Min and Max TSP(1, 2); 2.1. Introduction
2.1.1. The traveling salesman problem2.1.2. Multi-criteria optimization; 2.1.3. Organization of the chapter; 2.2. Overview; 2.3. The bicriteria TSP(1, 2); 2.3.1. Simple examples of the non-approximability; 2.3.2. A local search heuristic for the bicriteria TSP(1, 2); 2.3.3. A nearest neighbor heuristic for the bicriteria TSP(1, 2); 2.3.4. On the bicriteria Max TSP(1, 2); 2.4. k-criteria TSP(1, 2); 2.4.1. Non-approximability related to the number of generated solutions; 2.4.2. A nearest neighbor heuristic for the k-criteria TSP(1, 2); 2.5. Conclusion; 2.6. Bibliography
Chapter 3. Online Models for Set-covering: The Flaw of Greediness3.1. Introduction; 3.2. Description of the main results and related work; 3.3. The price of ignorance; 3.4. Competitiveness of TAKE-ALL and TAKE-AT-RANDOM; 3.4.1. TAKE-ALL algorithm; 3.4.2. TAKE-AT-RANDOM algorithm; 3.5. The nasty flaw of greediness; 3.6. The power of look-ahead; 3.7. The maximum budget saving problem; 3.8. Discussion; 3.9. Bibliography; Chapter 4. Comparison of Expressiveness for Timed Automata and Time Petri Nets; 4.1. Introduction; 4.2. Time Petri nets and timed automata
4.2.1. Timed transition systems and equivalence relations4.2.2. Time Petri nets; 4.2.3. Timed automata; 4.2.4. Expressiveness and equivalence problems; 4.3. Comparison of semantics I, A and PA; 4.3.1. A first comparison between the different semantics of TPNs; 4.3.2. A second comparison for standard bounded TPN; 4.4. Strict ordering results; 4.5. Equivalence with respect to timed language acceptance; 4.5.1. Encoding atomic constraints; 4.5.2. Resetting clocks; 4.5.3. The complete construction; 4.5.4. Δ (A) and A accept the same timed language; 4.5.5. Consequences of the previous results
4.6. Bisimulation of TA by TPNs4.6.1. Regions of a timed automaton; 4.6.2. From bisimulation to uniform bisimulation; 4.6.3. A characterization of bisimilarity; 4.6.4. Proof of necessity; 4.6.5. First construction; 4.6.6. Second construction; 4.6.7. Complexity results; 4.7. Conclusion; 4.8. Bibliography; Chapter 5. A "Maximum Node Clustering" Problem; 5.1. Introduction; 5.2. Approximation algorithm for the general problem; 5.3. The tree case; 5.3.1. Dynamic programming; 5.3.2. A fully polynomial time approximation scheme; 5.4. Exponential algorithms for special cases; 5.5. Bibliography
Chapter 6. The Patrolling Problem: Theoretical and Experimental Results
Record Nr. UNINA-9910139491403321
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Combinatorial optimization and theoretical computer science [[electronic resource] ] : interfaces and perspectives : 30th anniversary of the LAMSADE / / edited by Vangelis Th. Paschos
Combinatorial optimization and theoretical computer science [[electronic resource] ] : interfaces and perspectives : 30th anniversary of the LAMSADE / / edited by Vangelis Th. Paschos
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (518 p.)
Disciplina 519.6/4
519.64
Altri autori (Persone) PaschosVangelis Th
Collana ISTE
Soggetto topico Combinatorial optimization - Computer programs
Computer science - Mathematics
ISBN 1-282-16499-6
9786612164996
0-470-61109-X
0-470-39367-X
Classificazione SK 890
ST 130
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Combinatorial Optimization and Theoretical Computer Science; Contents; Preface; Chapter 1. The Complexity of Single Machine Scheduling Problems under Scenario-based Uncertainty; 1.1. Introduction; 1.2. Problem MinMax(1|prec|fmax, θ ); 1.2.1. Uncertainty on due dates; 1.2.2. Uncertainty on processing times and due dates; 1.3. Problem MinMax(1|| Σ wj Cj, Wj ); 1.4. Problem MinMax(1|| Σ Uj, θ ); 1.4.1. Uncertainty on due dates; 1.4.2. Uncertainty on processing times; 1.5. Bibliography; Chapter 2. Approximation of Multi-criteria Min and Max TSP(1, 2); 2.1. Introduction
2.1.1. The traveling salesman problem2.1.2. Multi-criteria optimization; 2.1.3. Organization of the chapter; 2.2. Overview; 2.3. The bicriteria TSP(1, 2); 2.3.1. Simple examples of the non-approximability; 2.3.2. A local search heuristic for the bicriteria TSP(1, 2); 2.3.3. A nearest neighbor heuristic for the bicriteria TSP(1, 2); 2.3.4. On the bicriteria Max TSP(1, 2); 2.4. k-criteria TSP(1, 2); 2.4.1. Non-approximability related to the number of generated solutions; 2.4.2. A nearest neighbor heuristic for the k-criteria TSP(1, 2); 2.5. Conclusion; 2.6. Bibliography
Chapter 3. Online Models for Set-covering: The Flaw of Greediness3.1. Introduction; 3.2. Description of the main results and related work; 3.3. The price of ignorance; 3.4. Competitiveness of TAKE-ALL and TAKE-AT-RANDOM; 3.4.1. TAKE-ALL algorithm; 3.4.2. TAKE-AT-RANDOM algorithm; 3.5. The nasty flaw of greediness; 3.6. The power of look-ahead; 3.7. The maximum budget saving problem; 3.8. Discussion; 3.9. Bibliography; Chapter 4. Comparison of Expressiveness for Timed Automata and Time Petri Nets; 4.1. Introduction; 4.2. Time Petri nets and timed automata
4.2.1. Timed transition systems and equivalence relations4.2.2. Time Petri nets; 4.2.3. Timed automata; 4.2.4. Expressiveness and equivalence problems; 4.3. Comparison of semantics I, A and PA; 4.3.1. A first comparison between the different semantics of TPNs; 4.3.2. A second comparison for standard bounded TPN; 4.4. Strict ordering results; 4.5. Equivalence with respect to timed language acceptance; 4.5.1. Encoding atomic constraints; 4.5.2. Resetting clocks; 4.5.3. The complete construction; 4.5.4. Δ (A) and A accept the same timed language; 4.5.5. Consequences of the previous results
4.6. Bisimulation of TA by TPNs4.6.1. Regions of a timed automaton; 4.6.2. From bisimulation to uniform bisimulation; 4.6.3. A characterization of bisimilarity; 4.6.4. Proof of necessity; 4.6.5. First construction; 4.6.6. Second construction; 4.6.7. Complexity results; 4.7. Conclusion; 4.8. Bibliography; Chapter 5. A "Maximum Node Clustering" Problem; 5.1. Introduction; 5.2. Approximation algorithm for the general problem; 5.3. The tree case; 5.3.1. Dynamic programming; 5.3.2. A fully polynomial time approximation scheme; 5.4. Exponential algorithms for special cases; 5.5. Bibliography
Chapter 6. The Patrolling Problem: Theoretical and Experimental Results
Record Nr. UNINA-9910830062503321
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Combinatorial optimization and theoretical computer science [[electronic resource] ] : interfaces and perspectives : 30th anniversary of the LAMSADE / / edited by Vangelis Th. Paschos
Combinatorial optimization and theoretical computer science [[electronic resource] ] : interfaces and perspectives : 30th anniversary of the LAMSADE / / edited by Vangelis Th. Paschos
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (518 p.)
Disciplina 519.6/4
519.64
Altri autori (Persone) PaschosVangelis Th
Collana ISTE
Soggetto topico Combinatorial optimization - Computer programs
Computer science - Mathematics
ISBN 1-282-16499-6
9786612164996
0-470-61109-X
0-470-39367-X
Classificazione SK 890
ST 130
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Combinatorial Optimization and Theoretical Computer Science; Contents; Preface; Chapter 1. The Complexity of Single Machine Scheduling Problems under Scenario-based Uncertainty; 1.1. Introduction; 1.2. Problem MinMax(1|prec|fmax, θ ); 1.2.1. Uncertainty on due dates; 1.2.2. Uncertainty on processing times and due dates; 1.3. Problem MinMax(1|| Σ wj Cj, Wj ); 1.4. Problem MinMax(1|| Σ Uj, θ ); 1.4.1. Uncertainty on due dates; 1.4.2. Uncertainty on processing times; 1.5. Bibliography; Chapter 2. Approximation of Multi-criteria Min and Max TSP(1, 2); 2.1. Introduction
2.1.1. The traveling salesman problem2.1.2. Multi-criteria optimization; 2.1.3. Organization of the chapter; 2.2. Overview; 2.3. The bicriteria TSP(1, 2); 2.3.1. Simple examples of the non-approximability; 2.3.2. A local search heuristic for the bicriteria TSP(1, 2); 2.3.3. A nearest neighbor heuristic for the bicriteria TSP(1, 2); 2.3.4. On the bicriteria Max TSP(1, 2); 2.4. k-criteria TSP(1, 2); 2.4.1. Non-approximability related to the number of generated solutions; 2.4.2. A nearest neighbor heuristic for the k-criteria TSP(1, 2); 2.5. Conclusion; 2.6. Bibliography
Chapter 3. Online Models for Set-covering: The Flaw of Greediness3.1. Introduction; 3.2. Description of the main results and related work; 3.3. The price of ignorance; 3.4. Competitiveness of TAKE-ALL and TAKE-AT-RANDOM; 3.4.1. TAKE-ALL algorithm; 3.4.2. TAKE-AT-RANDOM algorithm; 3.5. The nasty flaw of greediness; 3.6. The power of look-ahead; 3.7. The maximum budget saving problem; 3.8. Discussion; 3.9. Bibliography; Chapter 4. Comparison of Expressiveness for Timed Automata and Time Petri Nets; 4.1. Introduction; 4.2. Time Petri nets and timed automata
4.2.1. Timed transition systems and equivalence relations4.2.2. Time Petri nets; 4.2.3. Timed automata; 4.2.4. Expressiveness and equivalence problems; 4.3. Comparison of semantics I, A and PA; 4.3.1. A first comparison between the different semantics of TPNs; 4.3.2. A second comparison for standard bounded TPN; 4.4. Strict ordering results; 4.5. Equivalence with respect to timed language acceptance; 4.5.1. Encoding atomic constraints; 4.5.2. Resetting clocks; 4.5.3. The complete construction; 4.5.4. Δ (A) and A accept the same timed language; 4.5.5. Consequences of the previous results
4.6. Bisimulation of TA by TPNs4.6.1. Regions of a timed automaton; 4.6.2. From bisimulation to uniform bisimulation; 4.6.3. A characterization of bisimilarity; 4.6.4. Proof of necessity; 4.6.5. First construction; 4.6.6. Second construction; 4.6.7. Complexity results; 4.7. Conclusion; 4.8. Bibliography; Chapter 5. A "Maximum Node Clustering" Problem; 5.1. Introduction; 5.2. Approximation algorithm for the general problem; 5.3. The tree case; 5.3.1. Dynamic programming; 5.3.2. A fully polynomial time approximation scheme; 5.4. Exponential algorithms for special cases; 5.5. Bibliography
Chapter 6. The Patrolling Problem: Theoretical and Experimental Results
Record Nr. UNINA-9910840575603321
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Concepts of combinatorial optimization / / edited by Vangelis Th. Paschos
Concepts of combinatorial optimization / / edited by Vangelis Th. Paschos
Edizione [Revised and updated second edition.]
Pubbl/distr/stampa London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014
Descrizione fisica 1 online resource (409 p.)
Disciplina 519.64
Collana Mathematics and Statistics Series (ISTE)
Soggetto topico Combinatorial optimization
Programming (Mathematics)
ISBN 1-119-01507-3
1-119-00521-3
1-119-01518-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; Preface; PART I: Complexity of CombinatorialOptimization Problems; Chapter 1: Basic Concepts in Algorithmsand Complexity Theory; 1.1. Algorithmic complexity; 1.2. Problem complexity; 1.3. The classes P, NP and NPO; 1.4. Karp and Turing reductions; 1.5. NP-completeness; 1.6. Two examples of NP-complete problems; 1.6.1. MIN VERTEX COVER; 1.6.2. MAX STABLE; 1.7. A few words on strong and weak NP-completeness; 1.8. A few other well-known complexity classes; 1.9. Bibliography; Chapter 2: Randomized Complexity; 2.1. Deterministic and probabilistic algorithms
2.1.1. Complexity of a Las Vegas algorithm2.1.2. Probabilistic complexity of a problem; 2.2. Lower bound technique; 2.2.1. Definitions and notations; 2.2.2. Minimax theorem; 2.2.3. The Loomis lemma and the Yao principle; 2.3. Elementary intersection problem; 2.3.1. Upper bound; 2.3.2. Lower bound; 2.3.3. Probabilistic complexity; 2.4. Conclusion; 2.5. Bibliography; PART II: Classical Solution Methods; Chapter 3: Branch-and-Bound Methods; 3.1. Introduction; 3.2. Branch-and-bound method principles; 3.2.1. Principle of separation; 3.2.2. Pruning principles; 3.2.2.1. Bound
3.2.2.2. Evaluation function3.2.2.3. Use of the bound and of the evaluation function for pruning; 3.2.2.4. Other pruning principles; 3.2.2.5. Pruning order; 3.2.3. Developing the tree; 3.2.3.1. Description of development strategies; 3.2.3.2. Compared properties of the depth first and best first strategies; 3.3. A detailed example: the binary knapsack problem; 3.3.1. Calculating the initial bound; 3.3.2. First principle of separation; 3.3.3. Pruning without evaluation; 3.3.4. Evaluation; 3.3.5. Complete execution of the branch-and-bound method for finding only oneoptimal solution
3.3.6. First variant: finding all the optimal solutions3.3.7. Second variant: best first search strategy; 3.3.8. Third variant: second principle of separation; 3.4. Conclusion; 3.5. Bibliography; Chapter 4: Dynamic Programming; 4.1. Introduction; 4.2. A first example: crossing the bridge; 4.3. Formalization; 4.3.1. State space, decision set, transition function; 4.3.2. Feasible policies, comparison relationships and objectives; 4.4. Some other examples; 4.4.1. Stock management; 4.4.2. Shortest path bottleneck in a graph; 4.4.3. Knapsack problem; 4.5. Solution; 4.5.1. Forward procedure
4.5.2. Backward procedure4.5.3. Principles of optimality and monotonicity; 4.6. Solution of the examples; 4.6.1. Stock management; 4.6.2. Shortest path bottleneck; 4.6.3. Knapsack; 4.7. A few extensions; 4.7.1. Partial order and multicriteria optimization; 4.7.1.1. New formulation of the problem; 4.7.1.2. Solution; 4.7.1.3. Examples; 4.7.2. Dynamic programming with variables; 4.7.2.1. Sequential decision problems under uncertainty; 4.7.2.2. Solution; 4.7.2.3. Example; 4.7.3. Generalized dynamic programming; 4.8. Conclusion; 4.9. Bibliography; PART III: Elements from MathematicalProgramming
Chapter 5: Mixed Integer Linear Programming Models forCombinatorial Optimization Problems
Record Nr. UNINA-9910132156003321
London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Concepts of combinatorial optimization / / edited by Vangelis Th. Paschos
Concepts of combinatorial optimization / / edited by Vangelis Th. Paschos
Edizione [Revised and updated second edition.]
Pubbl/distr/stampa London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014
Descrizione fisica 1 online resource (409 p.)
Disciplina 519.64
Collana Mathematics and Statistics Series (ISTE)
Soggetto topico Combinatorial optimization
Programming (Mathematics)
ISBN 1-119-01507-3
1-119-00521-3
1-119-01518-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright; Contents; Preface; PART I: Complexity of CombinatorialOptimization Problems; Chapter 1: Basic Concepts in Algorithmsand Complexity Theory; 1.1. Algorithmic complexity; 1.2. Problem complexity; 1.3. The classes P, NP and NPO; 1.4. Karp and Turing reductions; 1.5. NP-completeness; 1.6. Two examples of NP-complete problems; 1.6.1. MIN VERTEX COVER; 1.6.2. MAX STABLE; 1.7. A few words on strong and weak NP-completeness; 1.8. A few other well-known complexity classes; 1.9. Bibliography; Chapter 2: Randomized Complexity; 2.1. Deterministic and probabilistic algorithms
2.1.1. Complexity of a Las Vegas algorithm2.1.2. Probabilistic complexity of a problem; 2.2. Lower bound technique; 2.2.1. Definitions and notations; 2.2.2. Minimax theorem; 2.2.3. The Loomis lemma and the Yao principle; 2.3. Elementary intersection problem; 2.3.1. Upper bound; 2.3.2. Lower bound; 2.3.3. Probabilistic complexity; 2.4. Conclusion; 2.5. Bibliography; PART II: Classical Solution Methods; Chapter 3: Branch-and-Bound Methods; 3.1. Introduction; 3.2. Branch-and-bound method principles; 3.2.1. Principle of separation; 3.2.2. Pruning principles; 3.2.2.1. Bound
3.2.2.2. Evaluation function3.2.2.3. Use of the bound and of the evaluation function for pruning; 3.2.2.4. Other pruning principles; 3.2.2.5. Pruning order; 3.2.3. Developing the tree; 3.2.3.1. Description of development strategies; 3.2.3.2. Compared properties of the depth first and best first strategies; 3.3. A detailed example: the binary knapsack problem; 3.3.1. Calculating the initial bound; 3.3.2. First principle of separation; 3.3.3. Pruning without evaluation; 3.3.4. Evaluation; 3.3.5. Complete execution of the branch-and-bound method for finding only oneoptimal solution
3.3.6. First variant: finding all the optimal solutions3.3.7. Second variant: best first search strategy; 3.3.8. Third variant: second principle of separation; 3.4. Conclusion; 3.5. Bibliography; Chapter 4: Dynamic Programming; 4.1. Introduction; 4.2. A first example: crossing the bridge; 4.3. Formalization; 4.3.1. State space, decision set, transition function; 4.3.2. Feasible policies, comparison relationships and objectives; 4.4. Some other examples; 4.4.1. Stock management; 4.4.2. Shortest path bottleneck in a graph; 4.4.3. Knapsack problem; 4.5. Solution; 4.5.1. Forward procedure
4.5.2. Backward procedure4.5.3. Principles of optimality and monotonicity; 4.6. Solution of the examples; 4.6.1. Stock management; 4.6.2. Shortest path bottleneck; 4.6.3. Knapsack; 4.7. A few extensions; 4.7.1. Partial order and multicriteria optimization; 4.7.1.1. New formulation of the problem; 4.7.1.2. Solution; 4.7.1.3. Examples; 4.7.2. Dynamic programming with variables; 4.7.2.1. Sequential decision problems under uncertainty; 4.7.2.2. Solution; 4.7.2.3. Example; 4.7.3. Generalized dynamic programming; 4.8. Conclusion; 4.9. Bibliography; PART III: Elements from MathematicalProgramming
Chapter 5: Mixed Integer Linear Programming Models forCombinatorial Optimization Problems
Record Nr. UNINA-9910821363403321
London, [England] ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Concepts of combinatorial optimization [[electronic resource] /] / edited by Vangelis Th. Paschos
Concepts of combinatorial optimization [[electronic resource] /] / edited by Vangelis Th. Paschos
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (382 p.)
Disciplina 519.64
Altri autori (Persone) PaschosVangelis Th
Collana ISTE
Combinatorial optimization
Soggetto topico Combinatorial optimization
Programming (Mathematics)
Soggetto genere / forma Electronic books.
ISBN 1-118-60024-X
1-118-60023-1
1-299-18744-7
1-118-60019-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto pt. I. Complexity of combinatorial optimization problems -- pt. II. Classical solution methods -- pt. III. Elements from mathematical programming.
Record Nr. UNINA-9910141489303321
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Concepts of combinatorial optimization [[electronic resource] /] / edited by Vangelis Th. Paschos
Concepts of combinatorial optimization [[electronic resource] /] / edited by Vangelis Th. Paschos
Pubbl/distr/stampa London, : ISTE
Descrizione fisica 1 online resource (382 p.)
Disciplina 519.64
Altri autori (Persone) PaschosVangelis Th
Collana ISTE
Combinatorial optimization
Soggetto topico Combinatorial optimization
Programming (Mathematics)
ISBN 1-118-60024-X
1-118-60023-1
1-299-18744-7
1-118-60019-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto pt. I. Complexity of combinatorial optimization problems -- pt. II. Classical solution methods -- pt. III. Elements from mathematical programming.
Record Nr. UNINA-9910830017403321
London, : ISTE
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui