Many people worldwide would agree that,had it not been for suffix trees,the simulation of journaling file systems might never have *** the current status of cooperative technology,system administrators daringly desire...
详细信息
Many people worldwide would agree that,had it not been for suffix trees,the simulation of journaling file systems might never have *** the current status of cooperative technology,system administrators daringly desire the study of digital-to-analog converters,which embodies the significant principles of operating *** our research we deconstruct randomized algorithms using *** prove that though the location-identity split and spreadsheets are always incompatible,wide-area networks can be made"fuzzy", ambimorphic,and compact.
We prove upper and lower bounds on the competitiveness of randomized algorithms for the list update problem of Sleator and Tarjan. We give a simple and elegant randomized algorithm that is more competitive than the be...
详细信息
We prove upper and lower bounds on the competitiveness of randomized algorithms for the list update problem of Sleator and Tarjan. We give a simple and elegant randomized algorithm that is more competitive than the best previous randomized algorithm due to Irani. Our algorithm uses randomness only during an initialization phase, and from then on runs completely deterministically. It is the first randomized competitive algorithm with this property to beat the deterministic lower bound. We generalize our approach to a model in which access costs are fixed but update costs are scaled by an arbitrary constant d. We prove lower bounds for deterministic list update algorithms and for randomized algorithms against oblivious and adaptive on-line adversaries. In particular, we show that for this problem adaptive on-line and adaptive off-line adversaries are equally powerful.
Quantum algorithms and complexity have recently been studied not only for discrete, but also for some numerical problems. Most attention has been paid so far to the integration and approximation problems, for which a ...
详细信息
Quantum algorithms and complexity have recently been studied not only for discrete, but also for some numerical problems. Most attention has been paid so far to the integration and approximation problems, for which a speed-up is shown in many important cases by quantum computers with respect to deterministic and randomized algorithms on a classical computer. In this paper, we deal with the randomized and quantum complexity of initial-value problems. For this nonlinear problem, we show that both randomized and quantum algorithms yield a speed-up over deterministic algorithms. Upper bounds on the complexity in the randomized and quantum settings are shown by constructing algorithms with a suitable cost, where the construction is based on integral information. Lower bounds result from the respective bounds for the integration problem. (C) 2004 Elsevier Inc. All rights reserved.
Many scholars would agree that,had it not been for the evaluation of gigabit switches,the analysis of massive multiplayer online role-playing games might never have *** fact,few experts would disagree with the intuiti...
详细信息
Many scholars would agree that,had it not been for the evaluation of gigabit switches,the analysis of massive multiplayer online role-playing games might never have *** fact,few experts would disagree with the intuitive unification of superblocks and the location-identity split,which embodies the confirmed principles of *** construct new heterogeneous communication(BACK),which we use to verify that red-black trees and the location-identity split are rarely incompatible.
In cloud systems, computation time can be rented by the hour and for a given number of processors. Thus, accurate predictions of the behaviour of both sequential and parallel algorithms has become an important issue, ...
详细信息
ISBN:
(纸本)9781509044603
In cloud systems, computation time can be rented by the hour and for a given number of processors. Thus, accurate predictions of the behaviour of both sequential and parallel algorithms has become an important issue, in particular in the case of costly methods such as randomized combinatorial optimization tools. In this work, our objective is to use machine learning to predict performance of sequential and parallel local search algorithms. In addition to classical features of the instances used by other machine learning tools, we consider data on the sequential runtime distributions of a local search method. This allows us to predict with a high accuracy the parallel computation time of a large class of instances, by learning the behaviour of the sequential version of the algorithm on a small number of instances. Experiments with three solvers on SAT and TSP instances indicate that our method works well, with a correlation coefficient of up to 0.85 for SAT instances and up to 0.95 for TSP instances.
How much can randomness help computation? Motivated by this general question and by volume computation. one of the few instances where randomness provably helps, we analyze a notion of dispersion and connect it to asy...
详细信息
How much can randomness help computation? Motivated by this general question and by volume computation. one of the few instances where randomness provably helps, we analyze a notion of dispersion and connect it to asymptotic convex geometry. We obtain a nearly quadratic lower bound on the complexity of randomized volume algorithms for convex bodies in R" (the current best algorithm has complexity roughly n(4), conjectured to be n(3)). Our main tools, dispersion of random determinants and dispersion of the length of a random point from a convex body. are of independent interest and applicable more generally in particular, the latter is closely related to the variance hypothesis from convex geometry. This geometric dispersion also leads to lower bounds for matrix problems and property testing. (C) 2008 Elsevier Inc. All rights reserved.
The multidimensional assignment problem (MAP) is a combinatorial optimization problem arising in diverse applications such as computer vision and motion tracking. In the MAP, the objective is to match tuples of object...
详细信息
The multidimensional assignment problem (MAP) is a combinatorial optimization problem arising in diverse applications such as computer vision and motion tracking. In the MAP, the objective is to match tuples of objects with minimum total cost. randomized parallel algorithms are proposed to solve MAPs appearing in multi-sensor multi-target applications. A parallel construction heuristic is described, together with some variations, as well as a parallel local search heuristic. Experimental results using the proposed algorithms are discussed. (C) 2003 IMACS. Published by Elsevier B.V. All rights reserved.
We consider the problem of solving a Laplacian system of equations Lx = b in a distributed fashion, where L is the Laplacian of the communication graph. Solving Laplacian systems arises in a number of applications inc...
详细信息
ISBN:
(纸本)9783952426937
We consider the problem of solving a Laplacian system of equations Lx = b in a distributed fashion, where L is the Laplacian of the communication graph. Solving Laplacian systems arises in a number of applications including consensus, distributed control, clock synchronization, localization and calculating effective resistances, to name a few. We leverage our analysis on a randomized variant of Kaczmarz's algorithm to propose a distributed asynchronous gossip algorithm with expected exponential convergence. We quantify the convergence rate depending solely on properties of the network topology, and further propose an accelerated version that scales favorably for larger networks. Our approach naturally extends to least-squares estimation of general linear systems where each row/column is assigned to nodes of a given network. Last but not least, we show that average consensus is a special case in our framework.
The multidimensional assignment problem (MAP) is a combinatorial optimization problem arising in diverse applications such as computer vision and motion tracking. In the MAP, the objective is to match tuples of object...
详细信息
The multidimensional assignment problem (MAP) is a combinatorial optimization problem arising in diverse applications such as computer vision and motion tracking. In the MAP, the objective is to match tuples of objects with minimum total cost. randomized parallel algorithms are proposed to solve MAPs appearing in multi-sensor multi-target applications. A parallel construction heuristic is described, together with some variations, as well as a parallel local search heuristic. Experimental results using the proposed algorithms are discussed. (C) 2003 IMACS. Published by Elsevier B.V. All rights reserved.
Using Jerabek's framework for probabilistic reasoning, we formalize the correctness of two fundamental RNC2 algorithms for bipartite perfect matching within the theory VPV for polytime reasoning. The first algorit...
详细信息
ISBN:
(纸本)9780769544120
Using Jerabek's framework for probabilistic reasoning, we formalize the correctness of two fundamental RNC2 algorithms for bipartite perfect matching within the theory VPV for polytime reasoning. The first algorithm is for testing if a bipartite graph has a perfect matching, and is based on the Schwartz-Zippel Lemma for polynomial identity testing applied to the Edmonds polynomial of the graph. The second algorithm, due to Mulmuley, Vazirani and Vazirani, is for finding a perfect matching, where the key ingredient of this algorithm is the Isolating Lemma.
暂无评论