The Experts below are selected from a list of 144 Experts worldwide ranked by ideXlab platform
Kazuhisa Makino - One of the best experts on this subject based on the ideXlab platform.
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Proportional cost buyback problem wIth weight bounds
Theoretical Computer Science, 2019Co-Authors: Yasushi Kawase, Xin Han, Kazuhisa MakinoAbstract:Abstract In this paper, we study the proportional cost buyback problem. The input is a sequence of Elements e 1 , e 2 , … , e n , each of which has a weight w ( e i ) . We assume that weights have an upper and a lower bound, i.e., l ≤ w ( e i ) ≤ u for any i. Given the Ith Element e i , we eIther accept e i or reject it wIth no cost, subject to some constraint on the set of accepted Elements. During the iterations, we could cancel some previously accepted Elements at a cost that is proportional to the total weight of them. Our goal is to maximize the profit, i.e., the sum of the weights of Elements kept until the end minus the total cancellation cost occurred. We consider a matroid and the unweighted knapsack constraints. For eIther case, we construct optimal online algorIthms and prove that they are the best possible.
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COCOA - Proportional Cost Buyback Problem wIth Weight Bounds
Combinatorial Optimization and Applications, 2015Co-Authors: Yasushi Kawase, Xin Han, Kazuhisa MakinoAbstract:In this paper, we study the proportional cost buyback problem. The input is a sequence of Elements $$e_1,e_2,\dots ,e_n$$e1,e2,i¾?,en, each of which has a weight $$we_i$$wei. We assume that weights have an upper and a lower bound, i.e., $$l\le we_i\le u$$l≤wei≤u for any $$i$$i. Given the Ith Element $$e_i$$ei, we eIther accept $$e_i$$ei or reject it wIth no cost, subject to some constraint on the set of accepted Elements. During the iterations, we could cancel some previously accepted Elements at a cost that is proportional to the total weight of them. Our goal is to maximize the profit, i.e., the sum of the weights of Elements kept until the end minus the total cancellation cost occurred. We consider the matroid and unweighted knapsack constraints. For eIther case, we construct optimal online algorIthms and prove that they are the best possible.
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ISAAC - Unit Cost Buyback Problem
Algorithms and Computation, 2013Co-Authors: Yasushi Kawase, Xin Han, Kazuhisa MakinoAbstract:In this paper, we study unit cost buyback problem, i.e., the buyback problem wIth fixed cancellation cost for each cancelled item. The input is a sequence of Elements e 1,e 2,…,e n , each of which has a weight w(e i ). We assume that weights have an upper and a lower bound, i.e., l ≤ w(e i ) ≤ u for any i. Given the Ith Element e i , we eIther accept e i or reject it wIth no cost, subject to some constraint on the set of accepted Elements. In order to accept a new Element e i , we could cancel some previous selected Elements at a cost which is proportional to the number of Elements canceled. Our goal is to maximize the profit, i.e., the sum of the weights of Elements accepted (and not canceled) minus the total cancellation cost occurred. We construct optimal online algorIthms and prove that they are the best possible, when the constraint is a matroid constraint or the unweighted knapsack constraint.
Yasushi Kawase - One of the best experts on this subject based on the ideXlab platform.
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Proportional cost buyback problem wIth weight bounds
Theoretical Computer Science, 2019Co-Authors: Yasushi Kawase, Xin Han, Kazuhisa MakinoAbstract:Abstract In this paper, we study the proportional cost buyback problem. The input is a sequence of Elements e 1 , e 2 , … , e n , each of which has a weight w ( e i ) . We assume that weights have an upper and a lower bound, i.e., l ≤ w ( e i ) ≤ u for any i. Given the Ith Element e i , we eIther accept e i or reject it wIth no cost, subject to some constraint on the set of accepted Elements. During the iterations, we could cancel some previously accepted Elements at a cost that is proportional to the total weight of them. Our goal is to maximize the profit, i.e., the sum of the weights of Elements kept until the end minus the total cancellation cost occurred. We consider a matroid and the unweighted knapsack constraints. For eIther case, we construct optimal online algorIthms and prove that they are the best possible.
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COCOA - Proportional Cost Buyback Problem wIth Weight Bounds
Combinatorial Optimization and Applications, 2015Co-Authors: Yasushi Kawase, Xin Han, Kazuhisa MakinoAbstract:In this paper, we study the proportional cost buyback problem. The input is a sequence of Elements $$e_1,e_2,\dots ,e_n$$e1,e2,i¾?,en, each of which has a weight $$we_i$$wei. We assume that weights have an upper and a lower bound, i.e., $$l\le we_i\le u$$l≤wei≤u for any $$i$$i. Given the Ith Element $$e_i$$ei, we eIther accept $$e_i$$ei or reject it wIth no cost, subject to some constraint on the set of accepted Elements. During the iterations, we could cancel some previously accepted Elements at a cost that is proportional to the total weight of them. Our goal is to maximize the profit, i.e., the sum of the weights of Elements kept until the end minus the total cancellation cost occurred. We consider the matroid and unweighted knapsack constraints. For eIther case, we construct optimal online algorIthms and prove that they are the best possible.
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ISAAC - Unit Cost Buyback Problem
Algorithms and Computation, 2013Co-Authors: Yasushi Kawase, Xin Han, Kazuhisa MakinoAbstract:In this paper, we study unit cost buyback problem, i.e., the buyback problem wIth fixed cancellation cost for each cancelled item. The input is a sequence of Elements e 1,e 2,…,e n , each of which has a weight w(e i ). We assume that weights have an upper and a lower bound, i.e., l ≤ w(e i ) ≤ u for any i. Given the Ith Element e i , we eIther accept e i or reject it wIth no cost, subject to some constraint on the set of accepted Elements. In order to accept a new Element e i , we could cancel some previous selected Elements at a cost which is proportional to the number of Elements canceled. Our goal is to maximize the profit, i.e., the sum of the weights of Elements accepted (and not canceled) minus the total cancellation cost occurred. We construct optimal online algorIthms and prove that they are the best possible, when the constraint is a matroid constraint or the unweighted knapsack constraint.
Xin Han - One of the best experts on this subject based on the ideXlab platform.
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Proportional cost buyback problem wIth weight bounds
Theoretical Computer Science, 2019Co-Authors: Yasushi Kawase, Xin Han, Kazuhisa MakinoAbstract:Abstract In this paper, we study the proportional cost buyback problem. The input is a sequence of Elements e 1 , e 2 , … , e n , each of which has a weight w ( e i ) . We assume that weights have an upper and a lower bound, i.e., l ≤ w ( e i ) ≤ u for any i. Given the Ith Element e i , we eIther accept e i or reject it wIth no cost, subject to some constraint on the set of accepted Elements. During the iterations, we could cancel some previously accepted Elements at a cost that is proportional to the total weight of them. Our goal is to maximize the profit, i.e., the sum of the weights of Elements kept until the end minus the total cancellation cost occurred. We consider a matroid and the unweighted knapsack constraints. For eIther case, we construct optimal online algorIthms and prove that they are the best possible.
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COCOA - Proportional Cost Buyback Problem wIth Weight Bounds
Combinatorial Optimization and Applications, 2015Co-Authors: Yasushi Kawase, Xin Han, Kazuhisa MakinoAbstract:In this paper, we study the proportional cost buyback problem. The input is a sequence of Elements $$e_1,e_2,\dots ,e_n$$e1,e2,i¾?,en, each of which has a weight $$we_i$$wei. We assume that weights have an upper and a lower bound, i.e., $$l\le we_i\le u$$l≤wei≤u for any $$i$$i. Given the Ith Element $$e_i$$ei, we eIther accept $$e_i$$ei or reject it wIth no cost, subject to some constraint on the set of accepted Elements. During the iterations, we could cancel some previously accepted Elements at a cost that is proportional to the total weight of them. Our goal is to maximize the profit, i.e., the sum of the weights of Elements kept until the end minus the total cancellation cost occurred. We consider the matroid and unweighted knapsack constraints. For eIther case, we construct optimal online algorIthms and prove that they are the best possible.
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ISAAC - Unit Cost Buyback Problem
Algorithms and Computation, 2013Co-Authors: Yasushi Kawase, Xin Han, Kazuhisa MakinoAbstract:In this paper, we study unit cost buyback problem, i.e., the buyback problem wIth fixed cancellation cost for each cancelled item. The input is a sequence of Elements e 1,e 2,…,e n , each of which has a weight w(e i ). We assume that weights have an upper and a lower bound, i.e., l ≤ w(e i ) ≤ u for any i. Given the Ith Element e i , we eIther accept e i or reject it wIth no cost, subject to some constraint on the set of accepted Elements. In order to accept a new Element e i , we could cancel some previous selected Elements at a cost which is proportional to the number of Elements canceled. Our goal is to maximize the profit, i.e., the sum of the weights of Elements accepted (and not canceled) minus the total cancellation cost occurred. We construct optimal online algorIthms and prove that they are the best possible, when the constraint is a matroid constraint or the unweighted knapsack constraint.
Lerna Pehlivan - One of the best experts on this subject based on the ideXlab platform.
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Structure of random 312-avoiding permutations†
Random Structures & Algorithms, 2015Co-Authors: Neal Madras, Lerna PehlivanAbstract:We evaluate the probabilities of various events under the uniform distribution on the set of 312-avoiding permutations of . We derive exact formulas for the probability that the Ith Element of a random permutation is a specific value less than i, and for joint probabilities of two such events. In addition, we obtain asymptotic approximations to these probabilities for large N when the Elements are not close to the boundaries or to each other. We also evaluate the probability that the graph of a random 312-avoiding permutation has k specified decreasing points, and we show that for large N the points below the diagonal look like trajectories of a random walk. © 2015 Wiley Periodicals, Inc. Random Struct. Alg., 2015
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Structure of Random 312-Avoiding Permutations
arXiv: Probability, 2014Co-Authors: Neal Madras, Lerna PehlivanAbstract:We evaluate the probabilities of various events under the uniform distribution on the set of 312-avoiding permutations of 1,...,N. We derive exact formulas for the probability that the Ith Element of a random permutation is a specific value less than i, and for joint probabilities of two such events. In addition, we obtain asymptotic approximations to these probabilities for large N when the Elements are not close to the boundaries or to each other. We also evaluate the probability that the graph of a random 312-avoiding permutation has k specified decreasing points, and we show that for large N the points below the diagonal look like trajectories of a random walk.
Majid Asadi - One of the best experts on this subject based on the ideXlab platform.
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Jensen–Shannon information of the coherent system lifetime
Reliability Engineering & System Safety, 2016Co-Authors: Majid Asadi, Nader Ebrahimi, Ehsan S. Soofi, Younes ZohrevandAbstract:The signature of a coherent system wIth n components is an n-dimensional vector whose Ith Element is the probability that the Ith failure of the components is fatal to the system. The signature depends only on the system design and provides useful tools for comparison of systems. We propose the Jensen–Shannon information (JS) criteria for comparison of systems, which is a scalar function of the signature and ranks systems based on their designs. The JS of a system is interpreted in terms of the remaining uncertainty about the system lifetime, the utility of dependence between the lifetime and the number of failures of components fatal to the system, and the Bayesian decision theory. The JS is non-negative and its minimum is attained by k-out-of-n systems, which are the least complex systems. This property offers JS as a measure of complexity of a system. Effects of expansion of a system on JS are studied. Application examples include comparisons of various sets of new systems and used but still working systems discussed in the literature. We also give an upper bound for the JS at the general level and compare it wIth a known upper bound.
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On the Failure Probability of Used Coherent Systems
Communications in Statistics - Theory and Methods, 2014Co-Authors: Majid Asadi, Amir R. AsadiAbstract:In analyzing the lifetime properties of a coherent system, the concept of “signature” is a useful tool. Let T be the lifetime of a coherent system having n iid components. The signature of the system is a probability vector s=(s1, s2, …, sn), such that si=P(T=Xi:n), where, Xi:n, i=1, 2, …, n denote the ordered lifetimes of the components. In this note, we assume that the system is working at time t>0. We consider the conditional signature of the system as a vector in which the Ith Element is defined as pi(t)=P(T=Xi:n|T>t) and investigate its properties as a function of time.
