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When the greedy algorithm fails
"... We provide a characterization of the cases when the greedy algorithm may produce the unique worst possible solution for the problem of finding a minimum weight base in a uniform independence system when the weights are taken from a finite range. We apply this theorem to TSP and the minimum bisection ..."
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Cited by 12 (3 self)
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We provide a characterization of the cases when the greedy algorithm may produce the unique worst possible solution for the problem of finding a minimum weight base in a uniform independence system when the weights are taken from a finite range. We apply this theorem to TSP and the minimum
Pushing the Envelope: Planning, Propositional Logic, and Stochastic Search
, 1996
"... Planning is a notoriously hard combinatorial search problem. In many interesting domains, current planning algorithms fail to scale up gracefully. By combining a general, stochastic search algorithm and appropriate problem encodings based on propositional logic, we are able to solve hard planning pr ..."
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Cited by 579 (33 self)
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Planning is a notoriously hard combinatorial search problem. In many interesting domains, current planning algorithms fail to scale up gracefully. By combining a general, stochastic search algorithm and appropriate problem encodings based on propositional logic, we are able to solve hard planning
A review of image denoising algorithms, with a new one
 SIMUL
, 2005
"... The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding perf ..."
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Cited by 508 (6 self)
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performance when the image model corresponds to the algorithm assumptions but fail in general and create artifacts or remove image fine structures. The main focus of this paper is, first, to define a general mathematical and experimental methodology to compare and classify classical image denoising algorithms
How the QR algorithm fails to converge and how to fix it
, 1996
"... In certain cases the shifted QR algorithm for real matrices converges slowly, if at all, when implemented in finite precision arithmetic. These obstacles are traced to a family of orthgonal similarity classes for which certain QR algorithms fail to converge on an open subset of a given orthogonal si ..."
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Cited by 8 (1 self)
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In certain cases the shifted QR algorithm for real matrices converges slowly, if at all, when implemented in finite precision arithmetic. These obstacles are traced to a family of orthgonal similarity classes for which certain QR algorithms fail to converge on an open subset of a given orthogonal
Distance metric learning, with application to clustering with sideinformation,”
 in Advances in Neural Information Processing Systems 15,
, 2002
"... Abstract Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as Kmeans initially fails to find one that is meaningful to a user, the only recourse may be for ..."
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Cited by 818 (13 self)
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Abstract Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as Kmeans initially fails to find one that is meaningful to a user, the only recourse may
Maté: A Tiny Virtual Machine for Sensor Networks
, 2002
"... Composed of tens of thousands of tiny devices with very limited resources ("motes"), sensor networks are subject to novel systems problems and constraints. The large number of motes in a sensor network means that there will often be some failing nodes; networks must be easy to repopulate. ..."
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Cited by 510 (21 self)
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Composed of tens of thousands of tiny devices with very limited resources ("motes"), sensor networks are subject to novel systems problems and constraints. The large number of motes in a sensor network means that there will often be some failing nodes; networks must be easy to repopu
Affine Scaling Algorithm Fails For Semidefinite Programming
 Mathematical Programming 83
, 1997
"... In this paper, we introduce an affine scaling algorithm for semidefinite programming, and give an example of a semidefinite program such that the affine scaling algorithm converges to a nonoptimal point. Both our program and its dual have interior feasible solutions, and unique optimal solutions wh ..."
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Cited by 6 (1 self)
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which satisfy strict complementarity, and they are nondegenerate everywhere. Abbreviated Title: Affine scaling fails for SDP Key Words: Semidefinite Programming, Affine Scaling Algorithm, Global Convergence Analysis. Affiliation: Department of Mechanical Engineering, Sophia University Address: 7
Where Replacement Algorithms Fail: a Thorough Analysis
"... Cache placement and eviction, especially at the last level of the memory hierarchy, have received a flurry of research activity recently. The common perception that LRU is a wellperforming algorithm has recently been discredited: many researchers have turned their attention to more sophisticated al ..."
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Cited by 3 (0 self)
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Cache placement and eviction, especially at the last level of the memory hierarchy, have received a flurry of research activity recently. The common perception that LRU is a wellperforming algorithm has recently been discredited: many researchers have turned their attention to more sophisticated
Achieving KAnonymity Privacy Protection Using Generalization and Suppression
 International Journal on Uncertainty, Fuzziness and Knowledgebased Systems
, 2002
"... This paper provides a formal presentation of combining generalization and suppression to achieve kanonymity. Generalization involves replacing (or recoding) a value with a less specific but semantically consistent value. Suppression involves not releasing a value at all. The Preferred Minimal Ge ..."
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Cited by 441 (3 self)
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Generalization Algorithm (MinGen), which is a theoretical algorithm presented herein, combines these techniques to provide kanonymity protection with minimal distortion. The realworld algorithms Datafly and Argus are compared to MinGen. Both Datafly and Argus use heuristics to make approximations, and so
A Parametric Texture Model based on Joint Statistics of Complex Wavelet Coefficients
 INTERNATIONAL JOURNAL OF COMPUTER VISION
, 2000
"... We present a universal statistical model for texture images in the context of an overcomplete complex wavelet transform. The model is parameterized by a set of statistics computed on pairs of coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. We de ..."
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Cited by 424 (13 self)
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develop an efficient algorithm for synthesizing random images subject to these constraints, by iteratively projecting onto the set of images satisfying each constraint, and we use this to test the perceptual validity of the model. In particular, we demonstrate the necessity of subgroups of the parameter
Results 1  10
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