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

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

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

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

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 K-means initially fails to find one that is meaningful to a user, the only recourse may

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

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

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 well-performing algorithm has recently been discredited: many researchers have turned their attention to more sophisticated

Generalization Algorithm (MinGen), which is a theoretical algorithm presented herein, combines these techniques to provide k-anonymity protection with minimal distortion. The real-world algorithms Datafly and -Argus are compared to MinGen. Both Datafly and -Argus use heuristics to make approximations, and so

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