Pub Date : 1900-01-01DOI: 10.1201/9781420010749.ch16
G. Ausiello, V. Paschos
{"title":"Differential Ratio Approximation","authors":"G. Ausiello, V. Paschos","doi":"10.1201/9781420010749.ch16","DOIUrl":"https://doi.org/10.1201/9781420010749.ch16","url":null,"abstract":"","PeriodicalId":262519,"journal":{"name":"Handbook of Approximation Algorithms and Metaheuristics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121112369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1201/9781351236423-24
L. Paquete, T. Stützle
{"title":"Stochastic Local Search Algorithms for Multiobjective Combinatorial Optimization: A Review","authors":"L. Paquete, T. Stützle","doi":"10.1201/9781351236423-24","DOIUrl":"https://doi.org/10.1201/9781351236423-24","url":null,"abstract":"","PeriodicalId":262519,"journal":{"name":"Handbook of Approximation Algorithms and Metaheuristics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129770809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1201/9781420010749.ch85
R. Battiti, Anurag Garg
In this report we provide an overview of digital reputation management systems for virtual communities. We begin with a discussion on the how trust is created in the real world. We then highlight how the process of trust creation differs in the virtual world. Next, we talk about the different ways in which digital reputations are useful to virtual communities such as incentivizing cooperation and punishing malicious users, providing recommendations or as a form of distributed authentication. We then discuss how evolutionary biology and game theory have informed research in reputation systems. This is followed by a discussion of the various components of reputation such as context, the forms of evidence, first and second order reputation and architectural considerations including the role of distributed hash tables in decentralized reputation management systems. We conclude with a discussion of some reputation management systems such as complaints-based trust, EigenTrust, PeerTrust and ROCQ.
{"title":"Digital Reputation for Virtual Communities","authors":"R. Battiti, Anurag Garg","doi":"10.1201/9781420010749.ch85","DOIUrl":"https://doi.org/10.1201/9781420010749.ch85","url":null,"abstract":"In this report we provide an overview of digital reputation management systems for virtual communities. We begin with a discussion on the how trust is created in the real world. We then highlight how the process of trust creation differs in the virtual world. Next, we talk about the different ways in which digital reputations are useful to virtual communities such as incentivizing cooperation and punishing malicious users, providing recommendations or as a form of distributed authentication. We then discuss how evolutionary biology and game theory have informed research in reputation systems. This is followed by a discussion of the various components of reputation such as context, the forms of evidence, first and second order reputation and architectural considerations including the role of distributed hash tables in decentralized reputation management systems. We conclude with a discussion of some reputation management systems such as complaints-based trust, EigenTrust, PeerTrust and ROCQ.","PeriodicalId":262519,"journal":{"name":"Handbook of Approximation Algorithms and Metaheuristics","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134248346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1201/9781420010749.ch47
J. Frank, A. Jónsson
Automated planning has been an active area of research in theoretical computer science and Artificial Intelligence (AI) for over 40 years. Planning is the study of general purpose algorithms that accept as input an initial state, a set of desired goal states, and a planning domain model that describes how actions can transform the state. The problem is to find a sequence of actions that transforms the initial state into one of the goal states. Planning is widely applicable, and has been used in such diverse application domains as spacecraft control [MNPW98], planetary rover operations [BJMR05], automated nursing aides [MP02], image processing [GPNV03], computer security [BGHH05] and automated manufacturing [RDF05]. Planning is also the subject of continued and lively ongoing research. In this chapter, we will present an overview of how approximations and related techniques are used in automated planning. We focus on classical planning problems, where states are conjunctions of propositions, all state information is known to the planner, and all action outcomes are deterministic. Classical planning is nonetheless a large problem class that generalizes many combinatorial problems including bin-packing ∗Universities Space Research Association †The authors gratefully acknowledge Sailesh Ramakrishnan, Ronen Brafman and Michael Freed for reviewing our early drafts
{"title":"Approximation Algorithms and Heuristics for Classical Planning","authors":"J. Frank, A. Jónsson","doi":"10.1201/9781420010749.ch47","DOIUrl":"https://doi.org/10.1201/9781420010749.ch47","url":null,"abstract":"Automated planning has been an active area of research in theoretical computer science and Artificial Intelligence (AI) for over 40 years. Planning is the study of general purpose algorithms that accept as input an initial state, a set of desired goal states, and a planning domain model that describes how actions can transform the state. The problem is to find a sequence of actions that transforms the initial state into one of the goal states. Planning is widely applicable, and has been used in such diverse application domains as spacecraft control [MNPW98], planetary rover operations [BJMR05], automated nursing aides [MP02], image processing [GPNV03], computer security [BGHH05] and automated manufacturing [RDF05]. Planning is also the subject of continued and lively ongoing research. In this chapter, we will present an overview of how approximations and related techniques are used in automated planning. We focus on classical planning problems, where states are conjunctions of propositions, all state information is known to the planner, and all action outcomes are deterministic. Classical planning is nonetheless a large problem class that generalizes many combinatorial problems including bin-packing ∗Universities Space Research Association †The authors gratefully acknowledge Sailesh Ramakrishnan, Ronen Brafman and Michael Freed for reviewing our early drafts","PeriodicalId":262519,"journal":{"name":"Handbook of Approximation Algorithms and Metaheuristics","volume":"33 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131990455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1201/9781420010749.ch13
A. Panconesi, F. Grandoni, Devdatt P. Dubhashi
The spread of computer networks, from sensor networks to the Internet, creates an ever-growing need for efficient distributed algorithms. In such scenarios, familiar combinatorial structures such as spanning trees and dominating sets are often useful for a variety of tasks. Others, like maximal independent sets, turn out to be a very useful primitive for computing other structures. In a distributed setting, where transmission of messages can be orders of magnitude slower than local computation, the expensive resource is communication. Therefore, the running time of an algorithm is given by the number of communication rounds that are needed by the algorithm. This will be made precise below.
{"title":"Distributed Approximation Algorithms via LP-Duality and Randomization","authors":"A. Panconesi, F. Grandoni, Devdatt P. Dubhashi","doi":"10.1201/9781420010749.ch13","DOIUrl":"https://doi.org/10.1201/9781420010749.ch13","url":null,"abstract":"The spread of computer networks, from sensor networks to the Internet, creates an ever-growing need for efficient distributed algorithms. In such scenarios, familiar combinatorial structures such as spanning trees and dominating sets are often useful for a variety of tasks. Others, like maximal independent sets, turn out to be a very useful primitive for computing other structures. In a distributed setting, where transmission of messages can be orders of magnitude slower than local computation, the expensive resource is communication. Therefore, the running time of an algorithm is given by the number of communication rounds that are needed by the algorithm. This will be made precise below.","PeriodicalId":262519,"journal":{"name":"Handbook of Approximation Algorithms and Metaheuristics","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123912842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1201/9781420010749.ch40
A. Marchetti-Spaccamela, V. Bonifaci, S. Leonardi, G. Ausiello
The most general version of the Prize Collecting Traveling Salesman Problem (PCTSP) was first introduced by Balas [8]. In this problem, a salesman has to collect a certain amount of prizes (the quota) by visiting cities. A known prize can be collected in every city. Furthermore, by not visiting a city, the salesman incurs a pecuniary penalty. The goal is to minimize the total travel distance plus the total penalty, while starting from a given city and collecting the quota. The problem generalizes both the Quota TSP, which is obtained when all the penalties are set to zero, and the Penalty TSP (sometimes unfortunately also called PCTSP), in which there is no required quota, only penalties. A special case of the Quota TSP is the k-TSP, in which all prizes are unitary (k is the quota). The k-TSP is strongly tied to the problem of finding a tree of minimum cost spanning any k vertices in a graph, called the k-MST problem. The k-MST and the k-TSP are NP-hard. They have been the subject of several studies for good approximation
{"title":"Prize-Collecting Traveling Salesman and Related Problems","authors":"A. Marchetti-Spaccamela, V. Bonifaci, S. Leonardi, G. Ausiello","doi":"10.1201/9781420010749.ch40","DOIUrl":"https://doi.org/10.1201/9781420010749.ch40","url":null,"abstract":"The most general version of the Prize Collecting Traveling Salesman Problem (PCTSP) was first introduced by Balas [8]. In this problem, a salesman has to collect a certain amount of prizes (the quota) by visiting cities. A known prize can be collected in every city. Furthermore, by not visiting a city, the salesman incurs a pecuniary penalty. The goal is to minimize the total travel distance plus the total penalty, while starting from a given city and collecting the quota. The problem generalizes both the Quota TSP, which is obtained when all the penalties are set to zero, and the Penalty TSP (sometimes unfortunately also called PCTSP), in which there is no required quota, only penalties. A special case of the Quota TSP is the k-TSP, in which all prizes are unitary (k is the quota). The k-TSP is strongly tied to the problem of finding a tree of minimum cost spanning any k vertices in a graph, called the k-MST problem. The k-MST and the k-TSP are NP-hard. They have been the subject of several studies for good approximation","PeriodicalId":262519,"journal":{"name":"Handbook of Approximation Algorithms and Metaheuristics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130831251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1201/9781420010749.ch15
G. Ausiello, V. Paschos
We present in this paper a tour d'horizon of the most important approximation-preserving reductions that have strongly influenced research about structure in approximability classes.
我们在这篇论文中展示了最重要的保持近似的约简，这些约简强烈地影响了关于近似类结构的研究。
{"title":"Reductions That Preserve Approximability","authors":"G. Ausiello, V. Paschos","doi":"10.1201/9781420010749.ch15","DOIUrl":"https://doi.org/10.1201/9781420010749.ch15","url":null,"abstract":"We present in this paper a tour d'horizon of the most important approximation-preserving reductions that have strongly influenced research about structure in approximability classes.","PeriodicalId":262519,"journal":{"name":"Handbook of Approximation Algorithms and Metaheuristics","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115135893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1201/9781420010749.ch27
Carlos Cotta, P. Moscato
The term ‘Memetic Algorithms’ [74] (MAs) was introduced in the late 80s to denote a family of metaheuristics that have as central theme the hybridization of different algorithmic approaches for a given problem. Special emphasis was given to the use of a population-based approach in which a set of cooperating and competing agents were engaged in periods of individual improvement of the solutions while they sporadically interact. Another main theme was to introduce problem and instance-dependent knowledge as a way of speeding-up the search process. Initially, hybridizations included Evolutionary Algorithms –EAs [35, 41, 89, 97], Simulated Annealing and its variants [52] [79] and Tabu Search [75] [9]. Today, a number of hybridizations include other metaheuristics [42] as well as exact algorithms, in complete anytime memetic algorithms [76]. These methods not only prove optimality, they can deliver high-quality solutions early on in the process. The adjective ‘memetic’ comes from the term ’meme’, coined by R. Dawkins [30] to denote an analogous to the gene in the context of cultural evolution. It was first proposed as a mean of conveying the message that, although inspiring for many, biological evolution should not constrain the imagination to develop
{"title":"Memetic Algorithms","authors":"Carlos Cotta, P. Moscato","doi":"10.1201/9781420010749.ch27","DOIUrl":"https://doi.org/10.1201/9781420010749.ch27","url":null,"abstract":"The term ‘Memetic Algorithms’ [74] (MAs) was introduced in the late 80s to denote a family of metaheuristics that have as central theme the hybridization of different algorithmic approaches for a given problem. Special emphasis was given to the use of a population-based approach in which a set of cooperating and competing agents were engaged in periods of individual improvement of the solutions while they sporadically interact. Another main theme was to introduce problem and instance-dependent knowledge as a way of speeding-up the search process. Initially, hybridizations included Evolutionary Algorithms –EAs [35, 41, 89, 97], Simulated Annealing and its variants [52] [79] and Tabu Search [75] [9]. Today, a number of hybridizations include other metaheuristics [42] as well as exact algorithms, in complete anytime memetic algorithms [76]. These methods not only prove optimality, they can deliver high-quality solutions early on in the process. The adjective ‘memetic’ comes from the term ’meme’, coined by R. Dawkins [30] to denote an analogous to the gene in the context of cultural evolution. It was first proposed as a mean of conveying the message that, although inspiring for many, biological evolution should not constrain the imagination to develop","PeriodicalId":262519,"journal":{"name":"Handbook of Approximation Algorithms and Metaheuristics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129327905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1201/9781420010749.ch31
Hans-Joachim Böckenhauer, S. Seibert, J. Hromkovic
{"title":"Stability of Approximation","authors":"Hans-Joachim Böckenhauer, S. Seibert, J. Hromkovic","doi":"10.1201/9781420010749.ch31","DOIUrl":"https://doi.org/10.1201/9781420010749.ch31","url":null,"abstract":"","PeriodicalId":262519,"journal":{"name":"Handbook of Approximation Algorithms and Metaheuristics","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127118230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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