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sequential algorithms for structural design optimization under tolerance conditions

ENGINEERING OPTIMIZATION 2014年第9期46卷 1183-1199页

作者： Chen, Jianqiao Tang, Yuanfu Huazhong Univ Sci & Technol Dept Mech Hubei Key Lab Engn Struct Anal & Safety Assessmen Wuhan 430074 Peoples R China

A deterministic optimization usually ignores the effects of uncertainties in design variables or design parameters on the constraints. In practical applications, it is required that the optimum solution can endure some tolerance so that the constraints are still satisfied when the solution undergoes variations within the tolerance range. An optimization problem under tolerance conditions is formulated in this article. It is a kind of robust design and a special case of a generalized semi-infinite programming (GSIP) problem. To overcome the deficiency of directly solving the double loop optimization, two sequential algorithms are then proposed for obtaining the solution, i.e. the double loop optimization is solved by a sequence of cycles. In each cycle a deterministic optimization and a worst case analysis are performed in succession. In sequential algorithm 1 (SA1), a shifting factor is introduced to adjust the feasible region in the next cycle, while in sequential algorithm 2 (SA2), the shifting factor is replaced by a shifting vector. Several examples are presented to demonstrate the efficiency of the proposed methods. An optimal design result based on the presented method can endure certain variation of design variables without violating the constraints. For GSIP, it is shown that SA1 can obtain a solution with equivalent accuracy and efficiency to a local reduction method (LRM). Nevertheless, the LRM is not applicable to the tolerance design problem studied in this article.

关键词： tolerance conditions robust design structural design optimization sequential algorithms GSIP

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sequential algorithms and strongly stable functions

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THEORETICAL COMPUTER SCIENCE 2005年第1-2期343卷 237-281页

作者： Melliès, PA Univ Paris 07 CNRS Equipe Preuves Programmes Syst UMR 7126 F-75251 Paris France

Intuitionistic proofs and PCF programs may be interpreted as functions between domains. or as strategies on games. The two kinds of interpretation are inherently different: static vs. dynamic, extensional vs. intentional. It is thus extremely instructive to compare and to connect them. In this article, we investigate the extensional content of the sequential algorithm hierarchy [-](SDS) introduced by Berry and Curien. We equip every sequential game [T](SDS) of the hierarchy with a realizability relation between plays and extensions. In this way, the sequential game [T](SDS) becomes a directed acyclic graph, instead of a tree. This enables to define a hypergraph [T](HC) on the extensions (or terminal leaves) of the game [T](SDS). We establish that the resulting hierarchy [-](HC) coincides with the strongly stable hierarchy introduced by Bucciarelli and Ehrhard. We deduce from this a game-theoretic proof of Ehrhard's collapse theorem, which states that the strongly stable hierarchy coincides with the extensional collapse of the sequential algorithm hierarchy. (c) 2005 Elsevier B.V. All rights reserved.

关键词： game semantics linear logic sequential algorithms strongly stable functions extensional collapse

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sequential algorithms for Unbounded Nondeterminism

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ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE 2015年 319卷 271-287页

作者： Laird, J. Univ Bath Dept Comp Sci Bath BA2 7AY Avon England

We give extensional and intensional characterizations of higher-order functional programs with unbounded nondeterminism: as stable and monotone functions between the biorders of states of ordered concrete data structures, and as sequential algorithms (states of an exponential ocds) which compute them. Our fundamental result establishes that these representations are equivalent, by showing how to construct a unique sequential algorithm which computes a given stable and monotone function. We illustrate by defining a denotational semantics for a functional language with countable nondeterminism ("fair PCF"), with an interpretation of fixpoints which allows this to be proved to be computationally adequate. We observe that our model contains functions which cannot be computed in fair PCF, by identifying a further property of the definable elements, and so show that it is not fully abstract.

关键词： sequential algorithms Nondeterminism Fairness Biorders

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sequential Randomized algorithms for Convex Optimization in the Presence of Uncertainty

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2016年第9期61卷 2565-2571页

作者： Chamanbaz, Mohammadreza Dabbene, Fabrizio Tempo, Roberto Venkataramanan, Venkatakrishnan Wang, Qing-Guo Data Storage Inst Singapore 138632 Singapore Natl Univ Singapore Dept Elect & Comp Engn Singapore 119077 Singapore Politecn Torino CNR IEIIT I-10129 Turin Italy

In this technical note, we propose new sequential randomized algorithms for convex optimization problems in the presence of uncertainty. A rigorous analysis of the theoretical properties of the solutions obtained by these algorithms, for full constraint satisfaction and partial constraint satisfaction, respectively, is given. The proposed methods allow to enlarge the applicability of the existing randomized methods to real-world applications involving a large number of design variables. Since the proposed approach does not provide a priori bounds on the sample complexity, extensive numerical simulations, dealing with an application to hard-disk drive servo design, are provided. These simulations testify the goodness of the proposed solution.

关键词： Convex optimization hard-disk servo design randomized algorithms sequential algorithms

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Efficient sequential and Parallel algorithms for Estimating Higher Order Spectra 19

Efficient Sequential and Parallel Algorithms for Estimating ...

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28th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Wang, Zigeng Mamun, Abdullah-Al Cai, Xingyu Ravishanker, Nalini Rajasekaran, Sanguthevar Univ Connecticut Dept Comp Sci & Engr Storrs CT 06269 USA Univ Connecticut Dept Stat Storrs CT 06269 USA Google LLC Mountain View CA USA

ISBN: (纸本)9781450369763

Higher order spectra (HOS) are a powerful tool in nonlinear time series analysis and they have been extensively used as feature representations in data mining, communications and cosmology domains. However, HOS estimation suffers from high computational cost and memory consumption. Any algorithm for computing the kth order spectra on a dataset of size n needs Omega(n(k-1)) time since the output size will be Omega(n(k-1)) as well, which makes the direct HOS analysis difficult for long time series, and further prohibits its direct deployment to resource-limited and time-sensitive applications. Existing algorithms for computing HOS are either inefficient or have been implemented on obsolete architectures. Thus it is essential to develop efficient generic algorithms for HOS estimations. In this paper, we present a package of generic sequential and parallel algorithms for computationally and memory efficient HOS estimations which can be employed on any parallel machine or platform. Our proposed algorithms largely reduce the HOS' computational cost and memory usage in spectrum multiplication and smoothing steps through carefully designed prefix sum operations. Moreover, we employ a matrix partitioning technique and design algorithms with optimal memory usage and present the parallel approaches on the PRAM and the mesh models. Furthermore, we implement our algorithms for both bispectrum and trispectrum estimations. We conduct extensive experiments and cross-compare the proposed algorithms' performance. Results show that our algorithms achieve state-of-the-art computational and memory efficiency, and our parallel algorithms achieve close to linear speedups.

关键词： higher order spectra sequential algorithms parallel algorithms memory efficient algorithms linear speedups

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ON RANDOMIZATION IN sequential AND DISTRIBUTED algorithms

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ACM COMPUTING SURVEYS 1994年第1期26卷 7-86页

作者： GUPTA, R SMOLKA, SA BHASKAR, S SUNY STONY BROOK DEPT COMP SCI STONY BROOK NY 11794 USA BELL NO RES RES TRIANGLE PK NC 27709 USA

Probabilistic, or randomized, algorithms are fast becoming as commonplace as conventional deterministic algorithms. This survey presents five techniques that have been widely used in the design of randomized algorithms. These techniques are illustrated using 12 randomized algorithms-both sequential and distributed-that span a wide range of applications, including: primality testing (a classical problem in number theory), universal hashing (choosing the hash function dynamically and at random), interactive probabilistic proof systems (a new method of program testing), dining philosophers (a classical problem in distributed computing), and Byzantine agreement (reaching agreement in the presence of malicious processors). Included with each algorithm is a discussion of its correctness and its computational complexity. Several related topics of interest are also addressed, including the theory of probabilistic automata, probabilistic analysis of conventional algorithms, deterministic amplification, and derandomization of randomized algorithms. Finally, a comprehensive annotated bibliography is given.

关键词： algorithms ANALYSIS OF algorithms BYZANTINE AGREEMENT COMPUTATIONAL COMPLEXITY CSP DINING PHILOSOPHERS PROBLEM DISTRIBUTED algorithms GRAPH ISOMORPHISM HASHING INTERACTIVE PROBABILISTIC PROOF SYSTEMS LEADER ELECTION MESSAGE ROUTING NEAREST-NEIGHBORS PROBLEM PERFECT HASHING PRIMALITY TESTING PROBABILISTIC TECHNIQUES RANDOMIZED OR PROBABILISTIC algorithms RANDOMIZED QUICKSORT sequential algorithms TRANSITIVE TOURNAMENTS UNIVERSAL HASHING

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sequential Data Assimilation algorithms for Air Quality Monitoring Models Based on a Weak-Constraint Variational Principle

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NUMERICAL ANALYSIS AND APPLICATIONS 2016年第4期9卷 312-325页

作者： Penenko, A. V. Penenko, V. V. Tsvetova, E. A. Russian Acad Sci Siberian Branch Inst Computat Math & Math Geophys Pr Akad Lavrenteva 6 Novosibirsk 630090 Russia

A data assimilation problem for unsteady models is considered as a sequence of coupled inverse problems of reconstruction of the space-time structure of the state functions with various sets of measurement data. The data assimilation is carried out jointly with the identification of an additional unknown function, which is interpreted as a function of model uncertainty. A variational principle serves as the basis for constructing the algorithms. Various versions of the algorithms are presented and analyzed. Based on the principle of the residual, a computationally efficient algorithm for data assimilation in a locally one-dimensional case is constructed. A theoretical estimate of its performance is obtained. This algorithm is one of the main components of a data assimilation system based on a splitting scheme for unsteady three-dimensional transport and transformation models of atmospheric chemistry.

关键词： data assimilation variational principle weak constraints direct and inverse problems model as regularizer sequential algorithms

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sequential clustering by statistical methodology

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PATTERN RECOGNITION LETTERS 2005年第14期26卷 2152-2163页

作者： Amador, JJ NASA John F Kennedy Space Ctr Kennedy Space Ctr FL 32899 USA

To overcome the limitation of requiring the cluster threshold with the parametric approach, this paper presents a clustering constraint which first considers an estimate of the global distribution. The clustering process moves from local clusters identifying the data globally to larger clusters with a specified density function. Merging then occurs to provide a statistically supported representation of the data. A hashing-based sequential clustering algorithm is introduced which utilizes the initial and merging constraints. Experimental data shows the methods effectiveness at classifying varying cluster shapes and sizes when compared to recent clustering techniques. Published by Elsevier B.V.

关键词： clustering sequential algorithms Gaussian distribution hashing

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A sequential algorithm for robust parameter estimation

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IEEE SIGNAL PROCESSING LETTERS 2005年第1期12卷 21-24页

作者： Pham, DS Zoubir, AM Curtin Univ Technol Commun & Signal Proc Grp Perth WA 6845 Australia TH Darmstadt Signal Proc Grp D-64283 Darmstadt Germany

A sequential M-estimation algorithm is proposed as an alternative to sequential least squares (LS). Being an approximation to exact M-estimation, the proposed technique is robust to non-Gaussian noise and outperforms sequential LS. A low-cost technique is introduced for initialization. We also show that sequential LS is a special case of the proposed algorithm.

关键词： M-estimation robust signal detection sequential algorithms

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sequential Karhunen-Loeve basis extraction and its application to images

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2000年第8期9卷 1371-1374页

作者： Levy, A Lindenbaum, M Technion Israel Inst Technol HP Labs Israel IL-32000 Haifa Israel Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

The Karhunen-Loeve (KL) Transform is an optimal method for approximating a set of vectors or images, which was used in image processing and computer vision for several tasks such as face and object recognition. Its computational demands and its batch calculation nature have limited its application. Here we present a new, sequential algorithm for calculating the KL basis, which is faster in typical applications and is especially advantageous for image sequences: the KL basis calculation is done with much lower delay and allows for dynamic updating of image databases. Systematic tests of the implemented algorithm show that these advantages are indeed obtained with the same accuracy available from batch KL algorithms.

关键词： Karhunen-Loeve Transform sequential algorithms singular value decomposition

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