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The NPcompleteness column: an ongoing guide
 Journal of Algorithms
, 1985
"... This is the nineteenth edition of a (usually) quarterly column that covers new developments in the theory of NPcompleteness. The presentation is modeled on that used by M. R. Garey and myself in our book ‘‘Computers and Intractability: A Guide to the Theory of NPCompleteness,’ ’ W. H. Freeman & ..."
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This is the nineteenth edition of a (usually) quarterly column that covers new developments in the theory of NPcompleteness. The presentation is modeled on that used by M. R. Garey and myself in our book ‘‘Computers and Intractability: A Guide to the Theory of NPCompleteness,’ ’ W. H. Freeman & Co., New York, 1979 (hereinafter referred to as ‘‘[G&J]’’; previous columns will be referred to by their dates). A background equivalent to that provided by [G&J] is assumed, and, when appropriate, crossreferences will be given to that book and the list of problems (NPcomplete and harder) presented there. Readers who have results they would like mentioned (NPhardness, PSPACEhardness, polynomialtimesolvability, etc.) or open problems they would like publicized, should
A Theoretician's Guide to the Experimental Analysis of Algorithms
, 1996
"... This paper presents an informal discussion of issues that arise when one attempts to analyze algorithms experimentally. It is based on lessons learned by the author over the course of more than a decade of experimentation, survey paper writing, refereeing, and lively discussions with other experimen ..."
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Cited by 85 (0 self)
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This paper presents an informal discussion of issues that arise when one attempts to analyze algorithms experimentally. It is based on lessons learned by the author over the course of more than a decade of experimentation, survey paper writing, refereeing, and lively discussions with other experimentalists. Although written from the perspective of a theoretical computer scientist, it is intended to be of use to researchers from all fields who want to study algorithms experimentally. It has two goals: first, to provide a useful guide to new experimentalists about how such work can best be performed and written up, and second, to challenge current researchers to think about whether their own work might be improved from a scientific point of view. With the latter purpose in mind, the author hopes that at least a few of his recommendations will be considered controversial.
Optimal Search and OneWay Trading Online Algorithms
 ALGORITHMICA
, 2001
"... This paper is concerned with the time series search and oneway trading problems. In the (time series) search problem a player is searching for the maximum (or minimum) price in a sequence that unfolds sequentially, one price at a time. Once during this game the player can decide to accept the curre ..."
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Cited by 36 (0 self)
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This paper is concerned with the time series search and oneway trading problems. In the (time series) search problem a player is searching for the maximum (or minimum) price in a sequence that unfolds sequentially, one price at a time. Once during this game the player can decide to accept the current price p in which case the game ends and the player's payoff is p.Intheoneway trading problem a trader is given the task of trading dollars to yen. Each day, a new exchange rate is announced and the trader must decide how many dollars to convert to yen according to the current rate. The game ends when the trader trades his entire dollar wealth to yen and his payoff is the number of yen acquired. The search and oneway trading are intimately related. Any (deterministic or randomized) oneway trading algorithm can be viewed as a randomized search algorithm. Using the competitive ratio as a performance measure we determine the optimal competitive performance for several variants of these problems. In particular, we show that a simple threatbased strategy is optimal and we determine its competitive ratio which yields, for realistic values of the problem parameters, surprisingly low competitive ratios. We also consider and analyze a oneway trading game played against an adversary called Nature where the online player knows the probability distribution of the maximum exchange rate and that distribution has been chosen by Nature. Finally, we consider some applications for a special case of portfolio selection called twoway trading in which the trader may trade back and forth between cash and one asset.
HyperHeuristics: Learning to Combine Simple Heuristics in BinPacking Problem
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Algorithms for the Online Travelling Salesman
 ALGORITHMICA
, 2001
"... In this paper the problem of efficiently serving a sequence of requests presented in an online fashion located at points of a metric space is considered. We call this problem the OnLine Travelling Salesman Problem (OLTSP). It has a variety of relevant applications in logistics and robotics. We ..."
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Cited by 31 (9 self)
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In this paper the problem of efficiently serving a sequence of requests presented in an online fashion located at points of a metric space is considered. We call this problem the OnLine Travelling Salesman Problem (OLTSP). It has a variety of relevant applications in logistics and robotics. We consider two
Learning a Procedure That Can Solve Hard BinPacking Problems: a new GAbased approach to hyperheuristics
, 2003
"... The idea underlying hyperheuristics is to discover some combination of familiar, straightforward heuristics that performs very well across a whole range of problems. To be worthwhile, such a combination should outperform all of the constituent heuristics. In this paper we describe a novel messyGA ..."
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Cited by 30 (3 self)
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The idea underlying hyperheuristics is to discover some combination of familiar, straightforward heuristics that performs very well across a whole range of problems. To be worthwhile, such a combination should outperform all of the constituent heuristics. In this paper we describe a novel messyGAbased approach that learns such a heuristic combination for solving onedimensional binpacking problems. When applied to a large set of benchmark problems, the learned procedure finds an optimal solution for nearly 80% of them, and for the rest produces an answer very close to optimal. When compared with its own constituent heuristics, it ranks first in 98% of the problems.
Smart SMART bounds for weighted response time scheduling
 SIAM Journal on Computing
, 1998
"... Consider a system of independent tasks to be scheduled without preemption on a parallel computer. For each task the number of processors required, the execution time, and a weight are known. The problem is to nd a schedule with minimum weighted average response time. We present an algorithm called S ..."
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Cited by 27 (5 self)
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Consider a system of independent tasks to be scheduled without preemption on a parallel computer. For each task the number of processors required, the execution time, and a weight are known. The problem is to nd a schedule with minimum weighted average response time. We present an algorithm called SMART for this problem that produces solutions that are within a factor of 10.45 of optimal. To our knowledge this is the rst polynomialtime algorithm for the minimum weighted average response time problem that achieves a constant bound. In addition, for the unweighted case (that is, where all the weights are unity) we describe a variant of SMART that produces solutions that are within a factor of 8 of optimal, improving upon the best known bound of 32 for this special case. 1
Dynamic Bin Packing
 SIAM J. COMPUT
, 1983
"... The average case analysis of algorithms usually assumes independent, identical distributions for the inputs. In [?], Kenyon introduced the randomorder ratio, a new average case performance metric for bin packing heuristics, and gave upper and lower bounds for it for he Best Fit heuristics. We intro ..."
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Cited by 24 (0 self)
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The average case analysis of algorithms usually assumes independent, identical distributions for the inputs. In [?], Kenyon introduced the randomorder ratio, a new average case performance metric for bin packing heuristics, and gave upper and lower bounds for it for he Best Fit heuristics. We introduce an alternative definition of the randomorder ratio and show that the two definitions give the same result for Next Fit. We also show that the randomorder ratio of Next Fit equals to its asymptotic worst case, i.e., it is 2. 2
The Statistical Adversary Allows Optimal MoneyMaking Trading Strategies (Extended Abstract)
, 1993
"... Andrew Chou Jeremy Cooperstock y Ran ElYaniv z Michael Klugerman x Tom Leighton  November, 1993 Abstract The distributional approach and competitive analysis have traditionally been used for the design and analysis of online algorithms. The former assumes a specific distribution on inputs, whil ..."
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Cited by 21 (4 self)
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Andrew Chou Jeremy Cooperstock y Ran ElYaniv z Michael Klugerman x Tom Leighton  November, 1993 Abstract The distributional approach and competitive analysis have traditionally been used for the design and analysis of online algorithms. The former assumes a specific distribution on inputs, while the latter assumes inputs are chosen by an unrestricted adversary. This paper employs the statistical adversary (recently proposed by Raghavan) to analyze and design online algorithms for twoway currency trading. The statistical adversary approach may be viewed as a hybrid of the distributional approach and competitive analysis. By statistical adversary, we mean an adversary that generates input sequences, where each sequence must satisfy certain general statistical properties. The online algorithms presented in this paper have some very attractive properties. For instance, the algorithms are moneymaking; they are guaranteed to be profitable when the optimal offli...
New Classes of Lower Bounds for Bin Packing Problems
, 1998
"... The bin packing problem is one of the classical NPhard optimization problems. Even though there are many excellent theoretical results, including polynomial approximation schemes, there is still a lack of methods that are able to solve practical instances optimally. In this paper, we present a fast ..."
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Cited by 16 (8 self)
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The bin packing problem is one of the classical NPhard optimization problems. Even though there are many excellent theoretical results, including polynomial approximation schemes, there is still a lack of methods that are able to solve practical instances optimally. In this paper, we present a fast and simple generic approach for obtaining new lower bounds, based on dual feasible functions. Worst case analysis as well as computational results show that one of our classes clearly outperforms the currently best known "economical" lower bound for the bin packing problem by Martello and Toth, which can be understood as a special case. This indicates the usefulness of our results in a branch and bound framework.