超立方体网络中任务调度的一个新近似算法

本文研究超立方体中的多处理器任务调度问题,我们研究LDLPT算法并指出为什么这种算法对一些实例具有最差的逼近度,然后提出一种类似装箱算法的新算法-BPA算法,证明该算法和LDLPT算法在相互最差逼近度中具有互补性质,最后,组合这两种算法的基本方法提出了一种求解问题的新算法-CBPA算法,并证明新算法具有比LDLPT算法更好的逼近度。     

A new hybrid optimization algorithm

We develop a new optimization algorithm that combines the genetic algorithm and a recently proposed global optimization algorithm called the nested partitions method. The resulting hybrid algorithm retains the global perspective of the nested partitions method and the local search capabilities of the genetic algorithm. We also present a detailed application of the new algorithm to a NP-hard product design problem and it is found empirically to outperform a pure genetic algorithm implementation, particularly for large problems.     

Energy optimal data propagation in wireless sensor networks

We propose an algorithm to compute the optimal parameters of a probabilistic data propagation algorithm for wireless sensor networks (WSN). The probabilistic data propagation algorithm we consider was introduced in previous work, and it is known that this algorithm, when used with adequate parameters, balances the energy consumption and increases the lifespan of the WSN. However, we show that in the general case achieving energy balance may not be possible. We propose a centralized algorithm to compute the optimal parameters of the probabilistic data propagation algorithm, and prove that these parameters maximize the lifespan of the network even when it is not possible to achieve energy balance. Compared to previous work, our contribution is the following: (a) we give a formal definition of an optimal data propagation algorithm: an algorithm maximizing the lifespan of the network. (b) We find a simple necessary and sufficient condition for the data propagation algorithm to be optimal. (c) We constructively prove that there exists a choice of parameters optimizing the probabilistic data propagation algorithm. (d) We provide a centralized algorithm to compute these optimal parameters, thus enabling their use in a WSN. (e) We extend previous work by considering the energy consumption per sensor, instead of the consumption per slice, and propose a spreading technique to balance the energy among sensors of a same slice. The technique is numerically validated by simulating a WSN accomplishing a data monitoring task and propagating data using the probabilistic data propagation algorithm with optimal parameters.     

TestAnt: An ant colony system approach to sequential testing under precedence constraints

We consider the problem of minimum cost sequential testing (diagnosis) of a series (or parallel) system under precedence constraints. We model the problem as a nonlinear integer program. We develop and implement an ant colony algorithm for the problem. We demonstrate the performance of this algorithm for special type of instances for which the optimal solutions can be found in polynomial time. In addition, we compare the performance of the ant colony algorithm with a branch and bound algorithm for randomly generated general instances of the problem. The ant colony algorithm is particularly effective as the problem size gets larger.     

一种随机化递增的压缩四叉树形数据结构的构建算法

介绍了一种压缩四叉树形数据结构的随机化增量构造算法。首先给出了压缩四叉树的定义,然后描述了算法实现步骤,通过将单位正方形不断分割为更小的正则正方形达到压缩的目的,使用平铺区域和冲突列表,采用随机化递增的算法构建出压缩四叉树,最后分析了算法正确性和运行时间。     

SEGMENTATION OF NATURAL IMAGES USING ANISOTROPIC DIFFUSION AND LINKING OF BOUNDARY EDGES

We present a segmentation method of natural images that uses an anisotropic diffusion algorithm and a region growing algorithm. We propose a modified version of the anisotropic diffusion algorithm as a precise edge-preserving smoothing technique modified by using boundary edges. We incorporate a linking algorithm for boundary edges based on a directional potential function into the anisotropic diffusion algorithm to improve the ability of edge-preserving smoothing. As a result, unnecessary details of images are effectively smoothed before performing a region growing algorithm. Therefore, the proposed method is suitable for an accurate segmentation of natural images. Several simulated examples are presented that demonstrate the effectiveness of the proposed technique.     

Training neural networks by stochastic optimisation

We present a stochastic learning algorithm for neural networks. The algorithm does not make any assumptions about transfer functions of individual neurons and does not depend on a functional form of a performance measure. The algorithm uses a random step of varying size to adapt weights. The average size of the step decreases during learning. The large steps enable the algorithm to jump over local maxima/minima, while the small ones ensure convergence in a local area. We investigate convergence properties of the proposed algorithm as well as test the algorithm on four supervised and unsupervised learning problems. We have found a superiority of this algorithm compared to several known algorithms when testing them on generated as well as real data.     

Tradeoffs in the empirical evaluation of competing algorithm designs

We propose an empirical analysis approach for characterizing tradeoffs between different methods for comparing a set of competing algorithm designs. Our approach can provide insight into performance variation both across candidate algorithms and across instances. It can also identify the best tradeoff between evaluating a larger number of candidate algorithm designs, performing these evaluations on a larger number of problem instances, and allocating more time to each algorithm run. We applied our approach to a study of the rich algorithm design spaces offered by three highly-parameterized, state-of-the-art algorithms for satisfiability and mixed integer programming, considering six different distributions of problem instances. We demonstrate that the resulting algorithm design scenarios differ in many ways, with important consequences for both automatic and manual algorithm design. We expect that both our methods and our findings will lead to tangible improvements in algorithm design methods.     

A genetic algorithm for sequential part assignment for PCB assembly

We present a genetic algorithm for printed circuit board (PCB) assembly, which simultaneously solves the feeder assignment and component sequencing problems. The algorithm uses a unique gene selection procedure that increases the convergence rate without degrading the solution quality. We have compared the performance of our algorithm with existing approaches and have demonstrated improved performance. We have also implemented the algorithm on a Quad IIIc insertion machine for surface mount components. The resulting suboptimal assembly times estimated by the algorithm have been found to be close to the actual optimal values.     

Model checking with bounded context switching

We discuss the implementation of a bounded context switching algorithm in the Spin model checker. The algorithm allows us to find counter-examples that are often simpler to understand, and that may be more likely to occur in practice. We discuss extensions of the algorithm that allow us to use this new algorithm in combination with most other search modes supported in Spin, including partial order reduction and bitstate hashing. We show that, other than often assumed, the enforcement of a bounded context switching discipline does not decrease but increases the complexity of the model checking procedure. We discuss the performance of the algorithm on a range of applications.     

Efficient implementation of parallel three-dimensional FFT on clusters of PCs

In this paper, we propose a high-performance parallel three-dimensional fast Fourier transform (FFT) algorithm on clusters of PCs. The three-dimensional FFT algorithm can be altered into a block three-dimensional FFT algorithm to reduce the number of cache misses. We show that the block three-dimensional FFT algorithm improves performance by utilizing the cache memory effectively. We use the block three-dimensional FFT algorithm to implement the parallel three-dimensional FFT algorithm. We succeeded in obtaining performance of over 1.3 GFLOPS on an 8-node dual Pentium III 1 GHz PC SMP cluster.     

Design and Performance of Parallel and Distributed Approximation Algorithms for Maxcut

We develop and experiment with a new parallel algorithm to approximate the maximum weight cut in a weighted undirected graph. Our implementation starts with the recent (serial) algorithm of Goemans and Williamson for this problem. We consider several different versions of this algorithm, varying the interior-point part of the algorithm in order to optimize the parallel efficiency of our method. Our work aims for an efficient, practical formulation of the algorithm with close-to-optimal parallelization. We analyze our parallel algorithm in the LogP model and predict linear speedup for a wide range of the parameters. We have implemented the algorithm using the message passing interface (MPI) and run it on several parallel machines. In particular, we present performance measurements on the IBM SP2, the Connection Machine CM5, and a cluster of workstations. We observe that the measured speedups are predicted well by our analysis in the LogP model. Finally, we test our implementation on several large graphs (up to 13,000 vertices), particularly on large instances of the Ising model.     

存储空间约束下物化视图的选择

介绍一种进化算法，通过利用并改进随机排列算法来实现在存储空间约束下物化视图的选择和物化视图维护代价的总和最小。该改进算法能有效地得到一个近似最优解的解决方案。最后介绍了该算法在公安数据仓库设计中的应用。     

Push-relabel based algorithms for the maximum transversal problem

We investigate the push-relabel algorithm for solving the problem of finding a maximum cardinality matching in a bipartite graph in the context of the maximum transversal problem. We describe in detail an optimized yet easy-to-implement version of the algorithm and fine-tune its parameters. We also introduce new performance-enhancing techniques. On a wide range of real-world instances, we compare the push-relabel algorithm with state-of-the-art algorithms based on augmenting paths and pseudoflows. We conclude that a carefully tuned push-relabel algorithm is competitive with all known augmenting path-based algorithms, and superior to the pseudoflow-based ones.     

The correctness of Newman’s typability algorithm and some of its extensions

We study Newman’s typability algorithm (Newman, 1943) [14] for simple type theory. The algorithm originates from 1943, but was left unnoticed until (Newman, 1943) [14] was recently rediscovered by Hindley (2008) [10]. The remarkable thing is that it decides typability without computing a type. We give a modern presentation of the algorithm (also a graphical one), prove its correctness and show that it implicitly does compute the principal type. We also show how the typing algorithm can be extended to other type constructors. Finally we show that Newman’s algorithm actually includes a unification algorithm.     

Quantum Partial Search of a Database with Several Target Items

We consider unstructured database separated into blocks of equal size. Blocks containing target items are called target blocks. Blocks without target items are called non-target blocks. We present a fast quantum algorithm, which finds one of the target blocks. The algorithm uses the same oracle, which the main Grover algorithm does. We study the simplest case, when each target block has the same number of target items. Our algorithm is based on Boyer, Brassard, Hoyer, and Tapp algorithm of searching database with several target items and on Grover–Radhakrishnan algorithm of partial search. We minimize the number of queries to the oracle. We analyze the algorithm for blocks of large size. In next publications we shall consider more general case when the number of target items is different in different target blocks.      

Predictive and comprehensible rule discovery using a multi-objective genetic algorithm

We present a multi-objective genetic algorithm for mining highly predictive and comprehensible classification rules from large databases. We emphasize predictive accuracy and comprehensibility of the rules. However, accuracy and comprehensibility of the rules often conflict with each other. This makes it an optimization problem that is very difficult to solve efficiently. We have proposed a multi-objective evolutionary algorithm called improved niched Pareto genetic algorithm (INPGA) for this purpose. We have compared the rule generation by INPGA with that by simple genetic algorithm (SGA) and basic niched Pareto genetic algorithm (NPGA). The experimental result confirms that our rule generation has a clear edge over SGA and NPGA.     

Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions

We consider on-line density estimation with a parameterized density from the exponential family. The on-line algorithm receives one example at a time and maintains a parameter that is essentially an average of the past examples. After receiving an example the algorithm incurs a loss, which is the negative log-likelihood of the example with respect to the current parameter of the algorithm. An off-line algorithm can choose the best parameter based on all the examples. We prove bounds on the additional total loss of the on-line algorithm over the total loss of the best off-line parameter. These relative loss bounds hold for an arbitrary sequence of examples. The goal is to design algorithms with the best possible relative loss bounds. We use a Bregman divergence to derive and analyze each algorithm. These divergences are relative entropies between two exponential distributions. We also use our methods to prove relative loss bounds for linear regression.     

An iterative algorithm learning the maximal margin classifier

A simple learning algorithm for maximal margin classifiers (also support vector machines with quadratic cost function) is proposed. We build our iterative algorithm on top of the Schlesinger-Kozinec algorithm (S-K-algorithm) from 1981 which finds a maximal margin hyperplane with a given precision for separable data. We suggest a generalization of the S-K-algorithm (i) to the non-linear case using kernel functions and (ii) for non-separable data. The requirement in memory storage is linear to the data. This property allows the proposed algorithm to be used for large training problems.The resulting algorithm is simple to implement and as the experiments showed competitive to the state-of-the-art algorithms. The implementation of the algorithm in Matlab is available. We tested the algorithm on the problem aiming at recognition poor quality numerals.     

“Meshsweeper”: dynamic point-to-polygonal mesh distanceand applications

We introduce a new algorithm for computing the distance from a point to an arbitrary polygonal mesh. Our algorithm uses a multiresolution hierarchy of bounding volumes generated by geometric simplification. Our algorithm is dynamic, exploiting coherence between subsequent queries using a priority process and achieving constant time queries in some cases. It can be applied to meshes that transform rigidly or deform nonrigidly. We illustrate our algorithm with a simulation of particle dynamics and collisions with a deformable mesh, the computation of distance maps and offset surfaces, the computation of an approximation to the expensive Hausdorff distance between two shapes, and the detection of self-intersections. We also report comparison results between our algorithm and an alternative algorithm using an octree, upon which our method permits an order-of-magnitude speed-up