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A deterministic algorithm for Alpha-Numeric Sequence Comparison with Application to Protein Sequence Detection

JOURNAL OF algorithmS & COMPUTATIONAL TECHNOLOGY 2015年第3期9卷 323-338页

作者： Kheniche, A. Brahimi, N. Salhi, A. Univ Essex Dept Math Sci Wivenhoe Pk Colchester CO4 3SQ Essex England Univ Abderrahmane Mira Dept Math Bejaia Algeria

This paper is an extension of a deterministic algorithm, [1, 2], that was initially designed to measure the rate of similarity between DNA sequences, and any sequences made up with symbols of alphabets of cardinality 4. Here, a modified and extended version to handle sequences of symbols from alphabets of cardinality > 4 is presented. This extension opens up its application area. As a test ground, we search for peptides within a protein database. Computational results on real data and a comparison with BLAST will be discussed.

关键词： BLAST deterministic algorithm Alpha-Numeric Sequence Numeration System Database

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An Enumeration-Based deterministic algorithm for Solving optimization Problem 6

An Enumeration-Based Deterministic Algorithm for Solving opt...

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Yang, Changlin Aeronautics Computing Technology Research Institute Process Management Office Xi'an China

ISBN: (纸本)9798350305623

An enumeration-based deterministic algorithm is proposed for solving optimization problem. Discretization of the feasible region for each decision variable is applied to establish search tree so that all the solutions could be enumerated. A new search region is established from the optimization of previous literation. Meanwhile, adjustment of search step with exponential decay function is conducive to reducing the search scope until the convergence of result is realized. The proposed algorithm is applied for three kinds of typical optimization problem, including Non-linear Programing Problem, Job-Shop Scheduling Problem and Traveling Salesman Problem. The results obtained by the new algorithm are compared with results of swarm intelligent optimization algorithm. Results indicate that for the combinational optimization, high convergence rate could be realized by adjusting the feasible region for each decision variable. For non-linear programming problem, the accuracy of results obtained from deterministic algorithm is better than that from self-organizing migrating algorithm, which infers that enumeration-based deterministic algorithm could be used to solve NP hard problem. © 2023 IEEE.

关键词： deterministic algorithm enumeration job-shop scheduling problem nonlinear programming problem optimization traveling salesman problem

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SpartaPlex: A deterministic algorithm with linear scalability for massively parallel global optimization of very large-scale problems

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ADVANCES IN ENGINEERING SOFTWARE 2022年第0期166卷 103090-103090页

作者： Albert, Benjamin Alexander Zhang, Arden Qiyu Johns Hopkins Univ Dept Biomed Engn Dept Comp Sci Baltimore MD 21218 USA Univ Maryland Dept Comp Sci Dept Math College Pk MD USA

SpartaPlex is a novel black-box optimization algorithm that yields superior results to state-of-the-art optimizers under tight function evaluation budgets. SpartaPlex is compared with 11 state-of-the-art optimization algorithms on 24 n-dimensional and 6 application-based benchmark problems including mechanical, structural, and antenna design optimization. Using identical computing resources, SpartaPlex finds superior objective function solutions and, as dimensionality increases, executes at least an order of magnitude faster than prior state-of-the-art. SpartaPlex is additionally evaluated for scalability using a compute cluster with 46,080 cores across 16 Graphical Processing Units. Tested in up to 100,000 dimensions, SpartaPlex demonstrates linear scalability in time with respect to dimensionality and computational resources.

关键词： Black-box optimization deterministic algorithm Global optimization Gpu clusters Large-scale optimization Linear scaling High-performance Computing Parallel scalability

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An accelerated deterministic algorithm for maximizing monotone submodular minus modular function with cardinality constraint

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THEORETICAL COMPUTER SCIENCE 2024年 1016卷

作者： Gong, Shufang Liu, Bin Fang, Qizhi Ocean Univ China Sch Math Sci Qingdao 266100 Shandong Peoples R China

Submodular optimization not only covers some classical combinatorial optimization problems, but also has a wide range of applications in fields such as machine learning and artificial intelligence. For submodular maximization problems with constraints, some work has been done including the design of approximation algorithms, the measurement of approximation algorithms in terms of quality and efficiency, etc. In this paper, we consider the problem of maximizing a non-negative monotone submodular function minus a non-negative modular function with the cardinality constraint. This model has been applied to many scenarios, such as team formation problem, influence maximization problem, recommender systems problem, etc. We propose a threshold algorithm that achieve a (1/2 -..(..), 2)- bicriteria approximation ratio and query complexity..(.. log..). Our algorithm makes a small sacrifice in the approximation ratio but improves the best query complexity result of existing deterministic algorithms from..(..2) to..(.. log..) in the worst case.

关键词： Submodular minus modular Cardinality constraint Accelerated algorithm deterministic algorithm

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Asynchronous approach in the plane: a deterministic polynomial algorithm

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DISTRIBUTED COMPUTING 2019年第4期32卷 317-337页

作者： Bouchard, Sebastien Bournat, Marjorie Dieudonne, Yoann Dubois, Swan Petit, Franck Sorbonne Univ CNRS INRIA LIP6 F-75005 Paris France Univ Picardie Jules Verne MIS Lab Amiens France

In this paper we study the task of approach of two mobile agents having the same limited range of vision and moving asynchronously in the plane. This task consists in getting them in finite time within each other's range of vision. The agents execute the same deterministic algorithm and are assumed to have a compass showing the cardinal directions as well as a unit measure. On the other hand, they do not share any global coordinates system (like GPS), cannot communicate and have distinct labels. Each agent knows its label but does not know the label of the other agent or the initial position of the other agent relative to its own. The route of an agent is a sequence of segments that are subsequently traversed in order to achieve approach. For each agent, the computation of its route depends only on its algorithm and its label. An adversary chooses the initial positions of both agents in the plane and controls the way each of them moves along every segment of the routes, in particular by arbitrarily varying the speeds of the agents. Roughly speaking, the goal of the adversary is to prevent the agents from solving the task, or at least to ensure that the agents have covered as much distance as possible before seeing each other. A deterministic approach algorithm is a deterministic algorithm that always allows two agents with any distinct labels to solve the task of approach regardless of the choices and the behavior of the adversary. The cost of a complete execution of an approach algorithm is the length of both parts of route travelled by the agents until approach is completed. Let Delta and l be the initial distance separating the agents and the length of (the binary representation of) the shortest label, respectively. Assuming that Delta andlare unknown to both agents, does there exist a deterministic approach algorithm always working at a cost that is polynomial in Delta andl? Actually the problem of approach in the plane reduces to the network problem of rendezvo

关键词： Mobile agents Asynchronous rendezvous Plane Infinite grid deterministic algorithm Polynomial cost

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deterministic approximation algorithm for submodular maximization subject to a matroid constraint

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INTERNATIONAL JOURNAL OF CARDIOLOGY 2021年 343卷 1-15页

作者： Sun, Xin Xu, Dachuan Guo, Longkun Li, Min Beijing Univ Technol Dept Operat Res & Informat Engn Beijing 100124 Peoples R China Qilu Univ Technol Shandong Comp Sci Ctr Sch Comp Sci & Technol Shandong Key Lab Comp NetworksShandong Acad Sci Jinan 250353 Peoples R China Fuzhou Univ Sch Comp & Data Sci Fuzhou 350116 Peoples R China Shandong Normal Univ Sch Math & Stat Jinan 250014 Peoples R China

In this paper, we study the generalized submodular maximization problem with a nonnegative monotone submodular set function as the objective function and subject to a matroid constraint. The problem is generalized through the curvature parameter alpha is an element of [0, 1] which measures how far a set function deviates from linearity to submodularity. We propose a deterministic approximation algorithm which uses the approximation algorithm proposed by Buchbinder et al. [2] as a building block and inherits the approximation guarantee for alpha = 1. For general value of the curvature parameter alpha is an element of [0, 1], we present an approximation algorithm with a factor of 1+h(alpha)(y)+Delta.[3+alpha-(2+alpha)y-(1+alpha)h(alpha)(y)]/2+alpha+(1+alpha)(1-y), where y is an element of [0, 1] is a predefined parameter for tuning the ratio. In particular, when alpha = 1 we obtain a ratio 0.5008 when setting y = 0.9, coinciding with the renowned state-of-art approximate ratio; when alpha = 0 that the object is a linear function, the approximation factor equals one and our algorithm is indeed an exact algorithm that always produces optimum solutions. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Approximation algorithm Submodular maximization Total curvature Matroid constraint deterministic algorithm ADAPTIVE COMPLEXITY SET OPTIMIZATION

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An O(n^1.5) deterministic gossiping algorithm for radio networks

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algorithmICA 2003年第1期36卷 93-96页

作者： Xu, Y Peking Univ Dept Comp Sci Beijing 100871 Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China

We consider the problem of distributed gossiping in radio networks of unknown topology. For radio networks of size n and diameter D, we present an adaptive deterministic gossiping algorithm of time O(NrootDn + n log(2) n) or O(n(1.5)). This algorithm is a tuned version of the fastest previously known gossiping algorithm due to Gasieniec and Lingas [1], and improves the time complexity by a poly-logarithmic factor.

关键词： gossiping radio network deterministic algorithm

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A deterministic Distributed algorithm for Reasoning with Connected Row-Convex Constraints 16

A Deterministic Distributed Algorithm for Reasoning with Con...

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16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

作者： Kong, Shufeng Lee, Jae Hee Li, Sanjiang Univ Technol Sydney Fac Engn & IT Ctr Quantum Software & Informat Sydney NSW Australia

ISBN: (纸本)9781510855076

The class of CRC constraints generalizes several tractable classes of constraints and is expressive enough to model problems in domains such as temporal reasoning, geometric reasoning, and scene labelling. This paper presents the first distributed deterministic algorithm for connected row-convex (CRC) constraints. Our distributed (partial) path consistency algorithm efficiently transforms a CRC constraint network into an equivalent constraint network, where all constraints are minimal (i.e., they are the tightest constraints) and generating all solutions can be done in a backtrackfree manner. When compared with the state-of-the-art distributed algorithm for CRC constraints, which is a randomized one, our algorithm guarantees to generate a solution for satisfiable CRC constraint networks and it is applicable to solve large networks in real distributed systems. The experimental evaluations show that our algorithm outperforms the state-of-the-art algorithm in both practice and theory.

关键词： Connected row-convex constraints deterministic algorithm distributed algorithm decomposable network

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A deterministic Distributed algorithm for Reasoning with Connected Row-Convex Constraints 17

A Deterministic Distributed Algorithm for Reasoning with Con...

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Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems

作者： Shufeng Kong Jae Hee Lee Sanjiang Li University of Technology Sydney Ultimo Australia

The class of CRC constraints generalizes several tractable classes of constraints and is expressive enough to model problems in domains such as temporal reasoning, geometric reasoning, and scene labelling. This paper presents the first distributed deterministic algorithm for connected row-convex (CRC) constraints. Our distributed (partial) path consistency algorithm efficiently transforms a CRC constraint network into an equivalent constraint network, where all constraints are minimal (i.e., they are the tightest constraints) and generating all solutions can be done in a backtrack-free manner. When compared with the state-of-the-art distributed algorithm for CRC constraints, which is a randomized one, our algorithm guarantees to generate a solution for satisfiable CRC constraint networks and it is applicable to solve large networks in real distributed systems. The experimental evaluations show that our algorithm outperforms the state-of-the-art algorithm in both practice and theory.

关键词： decomposable network connected row-convex constraints deterministic algorithm distributed algorithm

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Global optimization for large-scale water network synthesis based on dynamic partition and adaptive bound tightening

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AICHE JOURNAL 2025年第1期71卷

作者： Zhou, Wenjin Liu, Linlin Du, Jian Dalian Univ Technol Inst Chem Proc Syst Engn Sch Chem Engn Dalian 116024 Peoples R China

The synthesis of large-scale integrated water networks is typically formulated as nonconvex mixed-integer quadratic constrained programming (MIQCP) or QCP problems. With the complexity arising from bilinear terms in modeling mass flows of contaminants and binary variables representing the presence of units or streams, numerous local optima exist, thus presenting a significant optimization challenge. This study introduces a deterministic global optimization algorithm based on mixed-integer programming (MIP) to tackle such problems. The approach involves dynamically strengthening the relaxed problems to converge towards the original problems. A simultaneous partition strategy is proposed combining locally uniform division with dynamic partitioned variables choosing. Furthermore, several adaptive bound contraction schemes are introduced to efficiently manage the size of the relaxed problems, assisting in accelerating the solution process. The algorithm's effectiveness and robustness are demonstrated with a large test set, showing superior performance compared to commercial solvers specifically on MIQCP problems.

关键词： deterministic algorithm integrated process water systems mathematical programming optimal design piecewise McCormick relaxation

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