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23
Perspectives of Monge Properties in Optimization
, 1995
"... An m × n matrix C is called Monge matrix if c ij + c rs c is + c rj for all 1 i ! r m, 1 j ! s n. In this paper we present a survey on Monge matrices and related Monge properties and their role in combinatorial optimization. Specifically, we deal with the following three main topics: (i) funda ..."
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Cited by 54 (3 self)
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An m × n matrix C is called Monge matrix if c ij + c rs c is + c rj for all 1 i ! r m, 1 j ! s n. In this paper we present a survey on Monge matrices and related Monge properties and their role in combinatorial optimization. Specifically, we deal with the following three main topics: (i) fundamental combinatorial properties of Monge structures, (ii) applications of Monge properties to optimization problems and (iii) recognition of Monge properties.
Nearoptimal routing lookups with bounded worst case performance
 In IEEE INFOCOM’00
, 2000
"... Abstract — The problem of route address lookup has received much attention recently and several algorithms and data structures for performing address lookups at high speeds have been proposed. In this paper we consider one such data structure – a binary search tree built on the intervals created by ..."
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Cited by 28 (0 self)
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Abstract — The problem of route address lookup has received much attention recently and several algorithms and data structures for performing address lookups at high speeds have been proposed. In this paper we consider one such data structure – a binary search tree built on the intervals created by the routing table prefixes. We wish to exploit the difference in the probabilities with which the various leaves of the tree (where the intervals are stored) are accessed by incoming packets in order to speedup the lookup process. More precisely, we seek an answer to the question “How can the search tree be drawn so as to minimize the average packet lookup time while keeping the worstcase lookup time within a fixed bound? ” We use ideas from information theory to derive efficient algorithms for computing nearoptimal routing lookup trees. Finally, we consider the practicality of our algorithms through analysis and simulation.
A fast and spaceeconomical algorithm for lengthlimited coding
 Proc. Int. Symp. Algorithms and Computation, pp.1221
, 1995
"... Abstract. The minimumredundancy prefix code problem is to determine a list of integer codeword lengths I = [li l i E {1... n}], given a list of n symbol weightsp = [pili C {1.n}], such that ~' ~ 2l ' < 1, 9 " i = ln and ~i=1 lipi is minimised. An extension is the minimumredundancy lengthl ..."
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Cited by 15 (1 self)
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Abstract. The minimumredundancy prefix code problem is to determine a list of integer codeword lengths I = [li l i E {1... n}], given a list of n symbol weightsp = [pili C {1.n}], such that ~' ~ 2l ' < 1, 9 " i = ln and ~i=1 lipi is minimised. An extension is the minimumredundancy lengthlimited prefix code problem, in which the further constraint li < L is imposed, for all i C {1...n} and some integer L> [log 2 hi. The packagemerge algorithm of Larmore and Hirschberg generates lengthlimited codes in O(nL) time using O(n) words of auxiliary space. Here we show how the size of the work space can be reduced to O(L2). This represents a useful improvement, since for practical purposes L is O(log n). 1
Improved Bounds on the Inefficiency of LengthRestricted Prefix Codes
 Departamento de Inform'atica, PUCRJ, Rio de
, 1997
"... : Consider an alphabet \Sigma = fa 1 ; : : : ; ang with corresponding symbol probabilities p 1 ; : : : ; pn . The L\Gammarestricted prefix code is a prefix code where all the code lengths are not greater than L. The value L is a given integer such that L dlog ne. Define the average code length dif ..."
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Cited by 14 (5 self)
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: Consider an alphabet \Sigma = fa 1 ; : : : ; ang with corresponding symbol probabilities p 1 ; : : : ; pn . The L\Gammarestricted prefix code is a prefix code where all the code lengths are not greater than L. The value L is a given integer such that L dlog ne. Define the average code length difference by ffl = P n i=1 p i :l i \Gamma P n i=1 p i :l i , where l 1 ; : : : ; l n are the code lengths of the optimal Lrestricted prefix code for \Sigma and l 1 ; : : : ; l n are the code lengths of the optimal prefix code for \Sigma. Let / be the golden ratio 1,618. In this paper, we show that ffl ! 1=/ L\Gammadlog(n+dlog ne\GammaL)e\Gamma1 when L ? dlog ne. We also prove the sharp bound ffl ! dlog ne \Gamma 1, when L = dlog ne. By showing the lower bound 1 / L\Gammadlog ne+2+dlog n n\GammaL e \Gamma1 on the maximum value of ffl, we guarantee that our bound is asymptotically tight in the range dlog ne ! L n=2. Furthermore, we present an O(n) time and space 1=/ L\Gammadlo...
The WARMUP Algorithm: A Lagrangean Construction of Length Restricted Huffman Codes
 Departamento de Inform'atica, PUCRJ, Rio de
, 1996
"... : Given an alphabet fa 1 ; : : : ; ang with corresponding set of weights fw 1 ; : : : ; wng, and a number L dlog ne, we introduce an O(n log n+n log w) algorithm for constructing a suboptimal prefix code with restricted maximal length L, where w is the highest presented weight. The number of additi ..."
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Cited by 13 (8 self)
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: Given an alphabet fa 1 ; : : : ; ang with corresponding set of weights fw 1 ; : : : ; wng, and a number L dlog ne, we introduce an O(n log n+n log w) algorithm for constructing a suboptimal prefix code with restricted maximal length L, where w is the highest presented weight. The number of additional bits per symbol generated by our code is not greater than 1=/ L\Gammadlog(n+dlog ne\GammaL)e\Gamma2 when L ? dlog ne + 1, where / is the golden ratio 1:618. An important feature of the proposed algorithm is its implementation simplicity. The algorithm is basically a selected sequence of Huffman trees construction for modified weights. Keywords: Prefix codes, Huffman Trees, Lagragean Duality Resumo: Dado um alfabeto fa 1 ; : : : ; ang com pesos correspondentes fw 1 ; : : : ; wng e um n'umero L dlog ne, n'os apresentamoso um algoritmo de de complexidade O(n log n + n log w)para construit c'odigos de prefixo sub'otimos com restric~ao de comprimento L, onde w 'e o maior peso do dado co...
Lossless Compression for Text and Images
 International Journal of High Speed Electronics and Systems
, 1995
"... Most data that is inherently discrete needs to be compressed in such a way that it can be recovered exactly, without any loss. Examples include text of all kinds, experimental results, and statistical databases. Other forms of data may need to be stored exactly, such as imagesparticularly bilevel ..."
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Cited by 6 (0 self)
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Most data that is inherently discrete needs to be compressed in such a way that it can be recovered exactly, without any loss. Examples include text of all kinds, experimental results, and statistical databases. Other forms of data may need to be stored exactly, such as imagesparticularly bilevel ones, or ones arising in medical and remotesensing applications, or ones that may be required to be certified true for legal reasons. Moreover, during the process of lossy compression, many occasions for lossless compression of coefficients or other information arise. This paper surveys techniques for lossless compression. The process of compression can be broken down into modeling and coding. We provide an extensive discussion of coding techniques, and then introduce methods of modeling that are appropriate for text and images. Standard methods used in popular utilities (in the case of text) and international standards (in the case of images) are described. Keywords Text compression, ima...
Efficient Implementation of the WARMUP Algorithm for the Construction of LengthRestricted Prefix Codes
 in Proceedings of the ALENEX
, 1999
"... . Given an alphabet \Sigma = fa1 ; : : : ; ang with a corresponding list of positive weights fw1 ; : : : ; wng and a length restriction L, the lengthrestricted prefix code problem is to find, a prefix code that minimizes P n i=1 w i l i , where l i , the length of the codeword assigned to a i , ..."
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Cited by 5 (0 self)
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. Given an alphabet \Sigma = fa1 ; : : : ; ang with a corresponding list of positive weights fw1 ; : : : ; wng and a length restriction L, the lengthrestricted prefix code problem is to find, a prefix code that minimizes P n i=1 w i l i , where l i , the length of the codeword assigned to a i , cannot be greater than L, for i = 1; : : : ; n. In this paper, we present an efficient implementation of the WARMUP algorithm, an approximative method for this problem. The worstcase time complexity of WARMUP is O(n log n +n log wn ), where wn is the greatest weight. However, some experiments with a previous implementation of WARMUP show that it runs in linear time for several practical cases, if the input weights are already sorted. In addition, it often produces optimal codes. The proposed implementation combines two new enhancements to reduce the space usage of WARMUP and to improve its execution time. As a result, it is about ten times faster than the previous implementat...
Dynamic LengthRestricted Coding
, 2003
"... Suppose that $S$ is a string of length $m$ drawn from an alphabet of $n$ characters, $d$ of which occur in $S$. Let $P$ be the relative frequency distribution of characters in $S$. We present a new algorithm for dynamic coding that uses at most \(\lceil \lg n \rceil 1\) bits to encode each character ..."
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Cited by 4 (3 self)
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Suppose that $S$ is a string of length $m$ drawn from an alphabet of $n$ characters, $d$ of which occur in $S$. Let $P$ be the relative frequency distribution of characters in $S$. We present a new algorithm for dynamic coding that uses at most \(\lceil \lg n \rceil 1\) bits to encode each character in $S$
Twenty (or so) questions: Dary boundedlength Huffman coding,” preprint available from http://arxiv.org/abs/cs.IT/0602085
"... Abstract — The game of Twenty Questions has long been used to illustrate binary source coding. Recently, a physical device has been developed that mimics the process of playing Twenty Questions, with the device supplying the questions and the user providing the answers. However, this game differs fr ..."
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Cited by 3 (2 self)
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Abstract — The game of Twenty Questions has long been used to illustrate binary source coding. Recently, a physical device has been developed that mimics the process of playing Twenty Questions, with the device supplying the questions and the user providing the answers. However, this game differs from Twenty Questions in two ways: Answers need not be only “yes ” and “no, ” and the device continues to ask questions beyond the traditional twenty; typically, at least 20 and at most 25 questions are asked. The nonbinary variation on source coding is one that is well known and understood, but not with such bounds on length. An upper bound on the related property of fringe, the difference between the lengths of the longest and the shortest codewords, has been considered, but no polynomialtime algorithm currently finds optimal fringelimited codes. An O(n(lmax − lmin))time O(n)space PackageMergebased algorithm is presented here for finding an optimal Dary (binary or nonbinary) source code with all n codeword lengths (numbers of questions) bounded to be within the interval [lmin, lmax]. This algorithm minimizes average codeword length or, more generally, any other quasiarithmetic convex coding penalty. In the case of minimizing average codeword length, time complexity can often be improved via an alternative graphbased reduction. This has, as a special case, a method for nonbinary lengthlimited Huffman coding, which was previously solved via dynamic programming with O(n 2 lmax log D) time and O(n 2 log D) space. These algorithms can also be used to efficiently find a code that is optimal given a limit on fringe. I.