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5G Edge Computing : Technologies, Applications and Future Visions / / by Xiao Ma, Mengwei Xu, Qing Li, Yuanzhe Li, Ao Zhou, Shangguang Wang
| 5G Edge Computing : Technologies, Applications and Future Visions / / by Xiao Ma, Mengwei Xu, Qing Li, Yuanzhe Li, Ao Zhou, Shangguang Wang |
| Autore | Ma Xiao |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (209 pages) |
| Disciplina | 005.758 |
| Altri autori (Persone) |
XuMengwei
LiQing LiYuanzhe ZhouAo WangShangguang |
| Soggetto topico |
Mobile computing
Cloud computing Algorithms Electronic digital computers - Evaluation Computational complexity Mobile Computing Cloud Computing Design and Analysis of Algorithms System Performance and Evaluation Computational Complexity Algorismes Computació en núvol Complexitat computacional Informàtica mòbil |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 9789819702138 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Background -- Chapter 2. Recent Advancements of Public Edge Platforms -- Chapter 3. Edge Workload Prediction based on Deep Learning -- Chapter 4. Edge Computing based Computation Offloading -- Chapter 5. DynamicWorkload Scheduling in Edge Computing -- Chapter 6. Edge Service Caching -- Chapter 7. Edge Resource Provisioning -- Chapter 8. Edge Computing for 5G and 5G-based Mobile Edge Computing System -- Chapter 9. Visions of Edge Computing in 6G -- Chapter 10 Conclusions and Future Directions. |
| Record Nr. | UNINA-9910855384903321 |
Ma Xiao
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The ACM journal of experimental algorithmics
| The ACM journal of experimental algorithmics |
| Pubbl/distr/stampa | New York, : ACM |
| Disciplina | 004 |
| Soggetto topico |
Computer algorithms
Data structures (Computer science) Algorithms Algorismes Algorismes computacionals Estructures de dades (Informàtica) |
| Soggetto genere / forma |
Periodicals.
Revistes electròniques. |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
Journal of experimental algorithmics
JEA ACM JEA Association for Computing Machinery journal of experimental algorithmics |
| Record Nr. | UNINA-9910376057303321 |
| New York, : ACM | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algebraic Graph Algorithms : A Practical Guide Using Python / / by K. Erciyes
| Algebraic Graph Algorithms : A Practical Guide Using Python / / by K. Erciyes |
| Autore | Erciyes Kayhan |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (229 pages) |
| Disciplina | 511.5 |
| Collana | Undergraduate Topics in Computer Science |
| Soggetto topico |
Computer science
Computer science - Mathematics Discrete mathematics Theory of Computation Discrete Mathematics in Computer Science Mathematical Applications in Computer Science Python (Llenguatge de programació) Algorismes Àlgebra |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030878863
9783030878856 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction -- 2. Graphs, Matrices and Matroids -- 3. Parallel Matrix Algorithm Kernel -- 4. Basic Graph Algorithms -- 5. Connectivity, Matching and Matroids -- 6. Subgraph Search -- 7. Analysis of Large Graphs -- 8. Clustering in Complex Networks -- 9. Kronecker Graphs -- 10. Sample Algorithms for Complex Networks. |
| Record Nr. | UNINA-9910510574003321 |
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Erciyes Kayhan
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algorithm Portfolios [[electronic resource] ] : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos
| Algorithm Portfolios [[electronic resource] ] : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos |
| Autore | Souravlias Dimitris |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (xiv, 92 pages) : illustrations |
| Disciplina | 518.1 |
| Collana | SpringerBriefs in Optimization |
| Soggetto topico |
Operations research
Management science Algorithms Microprogramming Discrete mathematics Operations Research, Management Science Control Structures and Microprogramming Discrete Mathematics Algorismes Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-68514-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Metaheuristic optimization algorithms -- 2. Algorithm portfolios -- 3. Selection of constituent algorithms -- 4. Allocation of computation resources -- 5. Sequential and parallel models -- 6. Recent applications -- 7. Epilogue -- References. |
| Record Nr. | UNISA-996466562303316 |
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Souravlias Dimitris
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Algorithmic learning in a random world / / Vladimir Vovk, Alexander Gammerman, and Glenn Shafer
| Algorithmic learning in a random world / / Vladimir Vovk, Alexander Gammerman, and Glenn Shafer |
| Autore | Vovk Vladimir <1960-> |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (490 pages) |
| Disciplina | 518.1 |
| Soggetto topico |
Algorithms
Algorithms - Study and teaching Teoria de la predicció Algorismes Processos estocàstics |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-06649-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- Preface to the Second Edition -- Preface to the First Edition -- Notation and Abbreviations -- Sets, Bags, and Sequences -- Stochastics -- Machine Learning -- Programming -- Confidence Prediction -- Other Notations -- Abbreviations -- 1 Introduction -- 1.1 Machine Learning -- 1.1.1 Learning Under Randomness -- 1.1.2 Learning Under Unconstrained Randomness -- 1.2 A Shortcoming of Statistical Learning Theory -- 1.2.1 The Hold-Out Estimate of Confidence -- 1.2.2 The Contribution of This Book -- 1.3 The Online Framework -- 1.3.1 Online Learning -- 1.3.2 Online/Offline Compromises -- 1.3.3 One-Off and Offline Learning -- 1.3.4 Induction, Transduction, and the Online Framework -- 1.4 Conformal Prediction -- 1.4.1 Nested Prediction Sets -- 1.4.2 Validity -- 1.4.3 Efficiency -- 1.4.4 Conditionality -- 1.4.5 Flexibility of Conformal Predictors -- 1.5 Probabilistic Prediction Under Unconstrained Randomness -- 1.5.1 Universally Consistent Probabilistic Predictor -- 1.5.2 Probabilistic Prediction Using a Finite Dataset -- 1.5.3 Venn Prediction -- 1.5.4 Conformal Predictive Distributions -- 1.6 Beyond Randomness -- 1.6.1 Testing Randomness -- 1.6.2 Online Compression Models -- 1.7 Context -- Part I Set Prediction -- 2 Conformal Prediction: General Case and Regression -- 2.1 Confidence Predictors -- 2.1.1 Assumptions -- 2.1.2 Simple Predictors and Confidence Predictors -- 2.1.3 Validity -- 2.1.4 Randomized Confidence Predictors -- 2.1.5 Confidence Predictors Over a Finite Horizon -- 2.1.6 One-Off and Offline Confidence Predictors -- 2.2 Conformal Predictors -- 2.2.1 Bags -- 2.2.2 Nonconformity and Conformity -- 2.2.3 p-Values -- 2.2.4 Definition of Conformal Predictors -- 2.2.5 Validity -- 2.2.6 Smoothed Conformal Predictors -- 2.2.7 Finite-Horizon Conformal Prediction -- 2.2.8 One-Off and Offline Conformal Predictors.
2.2.9 General Schemes for Defining Nonconformity -- Conformity to a Bag -- Conformity to a Property -- 2.2.10 Deleted Conformity Measures -- 2.3 Conformalized Ridge Regression -- 2.3.1 Least Squares and Ridge Regression -- 2.3.2 Basic CRR -- 2.3.3 Two Modifications -- 2.3.4 Dual Form Ridge Regression -- 2.4 Conformalized Nearest Neighbours Regression -- 2.5 Efficiency of Conformalized Ridge Regression -- 2.5.1 Hard and Soft Models -- 2.5.2 Bayesian Ridge Regression -- 2.5.3 Efficiency of CRR -- 2.6 Are There Other Ways to Achieve Validity? -- 2.7 Conformal Transducers -- 2.7.1 Definitions and Properties of Validity -- 2.7.2 Normalized Confidence Predictors and Confidence Transducers -- 2.8 Proofs -- 2.8.1 Proof of Theorem 2.2 -- 2.8.2 Proof of Theorem 2.7 -- Regularizing the Rays in Upper CRR -- Proof Proper -- 2.8.3 Proof of Theorem 2.10 -- 2.9 Context -- 2.9.1 Exchangeability vs Randomness -- 2.9.2 Conformal Prediction -- 2.9.3 Two Equivalent Definitions of Nonconformity Measures -- 2.9.4 The Two Meanings of Conformity in Conformal Prediction -- 2.9.5 Examples of Nonconformity Measures -- 2.9.6 Kernel Methods -- 2.9.7 Burnaev-Wasserman Programme -- 2.9.8 Completeness Results -- 3 Conformal Prediction: Classification and General Case -- 3.1 Criteria of Efficiency for Conformal Prediction -- 3.1.1 Basic Criteria -- 3.1.2 Other Prior Criteria -- 3.1.3 Observed Criteria -- 3.1.4 Idealised Setting -- 3.1.5 Conditionally Proper Criteria of Efficiency -- 3.1.6 Criteria of Efficiency that Are not Conditionally Proper -- 3.1.7 Discussion -- 3.2 More Ways of Computing Nonconformity Scores -- 3.2.1 Nonconformity Scores from Nearest Neighbours -- 3.2.2 Nonconformity Scores from Support Vector Machines -- 3.2.3 Reducing Classification Problems to the Binary Case -- 3.3 Weak Teachers -- 3.3.1 Imperfectly Taught Predictors -- 3.3.2 Weak Validity. 3.3.3 Strong Validity -- 3.3.4 Iterated Logarithm Validity -- 3.3.5 Efficiency -- 3.4 Proofs -- 3.4.1 Proofs for Sect.3.1 -- Proof of Theorem 3.1 -- Proof of Theorem 3.2 -- Proof of Theorem 3.3 -- Proof of Theorem 3.4 -- 3.4.2 Proofs for Sect.3.3 -- Proof of Theorem 3.7, Part I -- Proof of Theorem 3.7, Part II -- Proof of Theorem 3.9 -- Proof of Theorem 3.13 -- 3.5 Context -- 3.5.1 Criteria of Efficiency -- 3.5.2 Examples of Nonconformity Measures -- 3.5.3 Universal Predictors -- 3.5.4 Weak Teachers -- 4 Modifications of Conformal Predictors -- 4.1 The Topics of This Chapter -- 4.2 Inductive Conformal Predictors -- 4.2.1 Inductive Conformal Predictors in the Online Mode -- 4.2.2 Inductive Conformal Predictors in the Offline and Semi-Online Modes -- 4.2.3 The General Scheme for Defining Nonconformity -- 4.2.4 Normalization and Hyper-Parameter Selection -- 4.3 Further Ways of Computing Nonconformity Scores -- 4.3.1 Nonconformity Measures Considered Earlier -- 4.3.2 De-Bayesing -- 4.3.3 Neural Networks and Other Multiclass Scoring Classifiers -- 4.3.4 Decision Trees and Random Forests -- 4.3.5 Binary Scoring Classifiers -- 4.3.6 Logistic Regression -- 4.3.7 Regression and Bootstrap -- 4.3.8 Training Inductive Conformal Predictors -- 4.4 Cross-Conformal Prediction -- 4.4.1 Definition of Cross-Conformal Predictors -- 4.4.2 Computational Efficiency -- 4.4.3 Validity and Lack Thereof for Cross-Conformal Predictors -- 4.5 Transductive Conformal Predictors -- 4.5.1 Definition -- 4.5.2 Validity -- 4.6 Conditional Conformal Predictors -- 4.6.1 One-Off Conditional Conformal Predictors -- 4.6.2 Mondrian Conformal Predictors and Transducers -- 4.6.3 Using Mondrian Conformal Transducers for Prediction -- 4.6.4 Generality of Mondrian Taxonomies -- 4.6.5 Conformal Prediction -- 4.6.6 Inductive Conformal Prediction -- 4.6.7 Label-Conditional Conformal Prediction. 4.6.8 Object-Conditional Conformal Prediction -- 4.7 Training-Conditional Validity -- 4.7.1 Conditional Validity -- 4.7.2 Training-Conditional Validity of Inductive Conformal Predictors -- 4.8 Context -- 4.8.1 Computationally Efficient Hedged Prediction -- 4.8.2 Specific Learning Algorithms and Nonconformity Measures -- 4.8.3 Training Conformal Predictors -- 4.8.4 Cross-Conformal Predictors and Alternative Approaches -- 4.8.5 Transductive Conformal Predictors -- 4.8.6 Conditional Conformal Predictors -- Part II Probabilistic Prediction -- 5 Impossibility Results -- 5.1 Introduction -- 5.2 Diverse Datasets -- 5.3 Impossibility of Estimation of Probabilities -- 5.3.1 Binary Case -- 5.3.2 Multiclass Case -- 5.4 Proof of Theorem 5.2 -- 5.4.1 Probability Estimators and Statistical Tests -- 5.4.2 Complete Statistical Tests -- 5.4.3 Restatement of the Theorem in Terms of Statistical Tests -- 5.4.4 The Proof of the Theorem -- 5.5 Context -- 5.5.1 More Advanced Results -- 5.5.2 Density Estimation, Regression Estimation, and Regression with Deterministic Objects -- 5.5.3 Universal Probabilistic Predictors -- 5.5.4 Algorithmic Randomness Perspective -- 6 Probabilistic Classification: Venn Predictors -- 6.1 Introduction -- 6.2 Venn Predictors -- 6.2.1 Validity of One-Off Venn Predictors -- 6.2.2 Are There Other Ways to Achieve Perfect Calibration? -- 6.2.3 Venn Prediction with Binary Labels and No Objects -- 6.3 A Universal Venn Predictor -- 6.4 Venn-Abers Predictors -- 6.4.1 Full Venn-Abers Predictors -- 6.4.2 Inductive Venn-Abers Predictors -- 6.4.3 Probabilistic Predictors Derived from Venn Predictors -- 6.4.4 Cross Venn-Abers Predictors -- 6.4.5 Merging Multiprobability Predictions into a Probabilistic Prediction -- 6.5 Proofs -- 6.5.1 Proof of Theorem 6.4 -- 6.5.2 PAVA and the Proof of Lemma 6.6 -- 6.5.3 Proof of Proposition 6.7 -- 6.6 Context. 6.6.1 Risk and Uncertainty -- 6.6.2 John Venn, Frequentist Probability, and the Problem of the Reference Class -- 6.6.3 Online Venn Predictors Are Calibrated -- 6.6.4 Isotonic Regression -- 7 Probabilistic Regression: Conformal Predictive Systems -- 7.1 Introduction -- 7.2 Conformal Predictive Systems -- 7.2.1 Basic Definitions -- 7.2.2 Properties of Validity -- 7.2.3 Simplest Example: Monotonic Conformity Measures -- 7.2.4 Criterion of Being a CPS -- 7.3 Least Squares Prediction Machine -- 7.3.1 Three Kinds of LSPM -- 7.3.2 The Studentized LSPM in an Explicit Form -- 7.3.3 The Offline Version of the Studentized LSPM -- 7.3.4 The Ordinary LSPM -- 7.3.5 Asymptotic Efficiency of the LSPM -- 7.3.6 Illustrations -- 7.4 Kernel Ridge Regression Prediction Machine -- 7.4.1 Explicit Forms of the KRRPM -- 7.4.2 Limitation of the KRRPM -- 7.5 Nearest Neighbours Prediction Machine -- 7.6 Universal Conformal Predictive Systems -- 7.6.1 Definitions -- 7.6.2 Universal Conformal Predictive Systems -- 7.6.3 Universal Deterministic Predictive Systems -- 7.7 Applications to Decision Making -- 7.7.1 A Standard Problem of Decision Making -- 7.7.2 Examples -- 7.7.3 Asymptotically Efficient Decision Making -- 7.7.4 Dangers of Overfitting -- 7.8 Computationally Efficient Versions -- 7.8.1 Inductive Conformal Predictive Systems -- 7.8.2 Cross-Conformal Predictive Distributions -- 7.8.3 Practical Aspects -- 7.8.4 Beyond Randomness -- 7.9 Proofs and Calculations -- 7.9.1 Proofs for Sect.7.2 -- Proof of Lemma 7.1 -- Proof of Proposition 7.2 -- 7.9.2 Proofs for Sect.7.3 -- Proof of Proposition 7.4 -- Proof of Proposition 7.5 -- Proof of Proposition 7.6 -- Proof of Proposition 7.7 -- Proof of Proposition 7.8 -- Computations for the Studentized LSPM -- The Ordinary LSPM -- Proof of (7.22) -- 7.9.3 Proof of Theorem 7.16 -- 7.9.4 Proofs for Sect.7.8 -- Proof of Proposition 7.17. Proof of Proposition 7.18. |
| Record Nr. | UNISA-996503551103316 |
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Vovk Vladimir <1960->
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| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Algorithmic puzzles / / Anany Levitin and Maria Levitin
| Algorithmic puzzles / / Anany Levitin and Maria Levitin |
| Autore | Levitin Anany |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Oxford ; ; New York, : Oxford University Press, c2011 |
| Descrizione fisica | 1 online resource (280 p.) |
| Disciplina | 793.74 |
| Altri autori (Persone) | LevitinMaria |
| Collana | Oxford scholarship online |
| Soggetto topico |
Mathematical recreations
Algorithms Algorismes Trencaclosques |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9786613299895
9780197563021 0197563023 9780199911776 0199911770 9781283299893 1283299895 9780199876549 0199876541 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover; Contents; Preface; Acknowledgments; List of Puzzles; Tutorial Puzzles; Main Section Puzzles; The Epigraph Puzzle: Who said what?; 1. Tutorials; General Strategies for Algorithm Design; Analysis Techniques; 2. Puzzles; Easier Puzzles (#1 to #50); Puzzles of Medium Difficulty (#51 to #110); Harder Puzzles (#111 to #150); 3. Hints; 4. Solutions; References; Design Strategy and Analysis Index; Index of Terms and Names; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; Q; R; S; T; V; W |
| Record Nr. | UNINA-9910960991503321 |
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Levitin Anany
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| Oxford ; ; New York, : Oxford University Press, c2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algorithmica
| Algorithmica |
| Pubbl/distr/stampa | New York : , : Springer-Verlag, , 1986- |
| Disciplina | 004 |
| Soggetto topico |
Electronic data processing
Computer algorithms Algorithmes Informatique Technologie de l'information Algorithmus Zeitschrift Online-Ressource Datenverarbeitung Algorismes |
| Soggetto genere / forma |
Periodicals.
Zeitschrift Online-Publikation Revistes electròniques. |
| ISSN | 1432-0541 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910146354403321 |
| New York : , : Springer-Verlag, , 1986- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algorithms and Computational Theory for Engineering Applications / / edited by Sripada Rama Sree, Sachin Kumar
| Algorithms and Computational Theory for Engineering Applications / / edited by Sripada Rama Sree, Sachin Kumar |
| Autore | Rama Sree Sripada |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (515 pages) |
| Disciplina | 518.1 |
| Altri autori (Persone) | KumarSachin (Computer scientist) |
| Collana | Advances in Science, Technology & Innovation, IEREK Interdisciplinary Series for Sustainable Development |
| Soggetto topico |
Algorithms
Engineering mathematics Engineering - Data processing Information technology - Management Mathematical and Computational Engineering Applications Computer Application in Administrative Data Processing Algorismes Tecnologia de la informació Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031727474
3031727479 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Optimization Techniques -- 2. Machine Learning and Artificial Intelligence -- 3. Data Science and Big Data Analytics -- 4. Computational Modelling and Simulation -- 5. Robotics and Control Systems. |
| Record Nr. | UNINA-9910983337503321 |
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Rama Sree Sripada
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Algorithms and Discrete Applied Mathematics [[electronic resource] ] : 7th International Conference, CALDAM 2021, Rupnagar, India, February 11–13, 2021, Proceedings / / edited by Apurva Mudgal, C. R. Subramanian
| Algorithms and Discrete Applied Mathematics [[electronic resource] ] : 7th International Conference, CALDAM 2021, Rupnagar, India, February 11–13, 2021, Proceedings / / edited by Apurva Mudgal, C. R. Subramanian |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (519 pages) |
| Disciplina | 004.0151 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Computer science—Mathematics
Algorithms Data structures (Computer science) Information theory Mathematics—Data processing Mathematics of Computing Design and Analysis of Algorithms Data Structures and Information Theory Computational Mathematics and Numerical Analysis Algorismes Geometria Teoria de grafs |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-67899-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Approximation Algorithms -- Online bin packing with overload cost -- Scheduling Trains with Small Stretch on a Unidirectional Line -- Algorithmic Aspects of Total Roman and Total Double Roman Domination in Graphs -- Approximation Algorithms for Orthogonal Line Centers -- Semitotal Domination on AT-free Graphs and Circle Graphs -- Burning Grids and Intervals -- Parameterized Algorithms -- On Parameterized Complexity of Liquid Democracy -- Acyclic coloring parameterized by directed clique-width. - On Structural Parameterizations of Load Coloring -- One-Sided Discrete Terrain Guarding and Chordal Graphs -- Parameterized Complexity of Locally Minimal Defensive Alliances -- Computational Geometry -- New variants of Perfect Non-crossing Matchings -- Cause I’m a Genial Imprecise Point: Outlier Detection for Uncertain Data -- A Worst-case Optimal Algorithm to Compute the Minkowski Sum of Convex Polytopes -- On the Intersections of Non-homotopic Loops -- Graph Theory -- On cd-coloring of trees and co-bipartite graphs -- Cut Vertex Transit Functions of Hypergraphs -- Lexicographic Product of Digraphs and Related Boundary-Type Sets -- The Connected Domination Number of Grids -- On degree sequences and eccentricities in pseudoline arrangement graphs. - Cops and Robber on Butterflies and Solid Grids -- b-Coloring of Some Powers of Hypercubes -- Chromatic Bounds for the Subclasses of $pK_2$ -Free Graphs -- Axiomatic characterization of the median function of a block graph -- On Coupon Coloring of Cartesian Product of Some Graphs -- On the Connectivity and the Diameter of Betweenness-Uniform Graphs. -Combinatorics and Algorithms -- On algorithms to find p-ordering -- Experimental Evaluation of a Local Search Approximation Algorithm for the Multiway Cut Problem -- Algorithmic analysis of priority-based bin packing -- Recursive methods for some problems in coding and random permutations -- Achieving positive rates with predetermined dictionaries -- Characterization of Dense Patterns Having Distinct Squares -- Graph Algorithms -- Failure and communication in a synchronized multi-drone system -- Memory Optimal Dispersion by Anonymous Mobile Robots -- Quantum and approximation algorithms for maximum witnesses of Boolean matrix products. -Template-driven Rainbow Coloring of Proper Interval Graphs -- Minimum Consistent Subset of Simple Graph Classes. - Computational Complexity -- Balanced Connected Graph Partition -- Hardness Results of Global Roman Domination in Graphs. . |
| Record Nr. | UNISA-996464385203316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Algorithms for Constructing Computably Enumerable Sets / / by Kenneth J. Supowit
| Algorithms for Constructing Computably Enumerable Sets / / by Kenneth J. Supowit |
| Autore | Supowit Kenneth J. |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2023 |
| Descrizione fisica | 1 online resource (191 pages) |
| Disciplina | 004.0151 |
| Collana | Computer Science Foundations and Applied Logic |
| Soggetto topico |
Computer science
Computable functions Recursion theory Set theory Computer science—Mathematics Theory of Computation Computability and Recursion Theory Set Theory Theory and Algorithms for Application Domains Mathematics of Computing Matemàtica discreta Teoria de conjunts Algorismes |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031269042
9783031269035 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Index of notation and terms -- 2 Set theory, requirements, witnesses -- 3 What’s new in this chapter? -- 4 Priorities (a splitting theorem) -- 5 Reductions, comparability (Kleene-Post Theorem) -- 6 Finite injury (Friedberg-Muchnik Theorem) -- 7 The Permanence Lemma -- 8 Permitting (Friedberg-Muchnik below C Theorem) -- 9 Length of agreement (Sacks Splitting Theorem) -- 10 Introduction to infinite injury -- 11 A tree of guesses (Weak Thickness Lemma) -- 12 An infinitely branching tree (Thickness Lemma) -- 13 True stages (another proof of the Thickness Lemma) -- 14 Joint custody (Minimal Pair Theorem) -- 15 Witness lists (Density Theorem) -- 16 The theme of this book: delaying tactics -- Appendix A: a pairing function -- Bibliograph -- Solutions to selected exercises. |
| Record Nr. | UNINA-9910726280003321 |
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Supowit Kenneth J.
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| Cham : , : Springer International Publishing : , : Imprint : Birkhäuser, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Soggetto topico
-
Algorismes
(28)
- Algorithms (18)
- Mathematical optimization (5)
- Optimització matemàtica (5)
- Processament de dades (5)