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3rd ACM SIGSPATIAL international workshop on Spatial Big Data and AI for Industrial applications

作者： Wang, Shaohua Xu, Dachuan Zhou, Junyuan Su, Cheng Li, Xiao Liang, Xiaojian Cao, Chunxiang Liu, Chang Zhong, Yang CAS Beijing Aerosp Informat Res Inst Beijing 10094 Peoples R China Lanzhou Jiaotong Univ Fac Geomat Lanzhou 730070 Peoples R China CGU Claremont Sch Informat Syst & Technol Claremont CA 91711 USA

ISBN: (纸本)9798400711459

Convenience stores, as a rapidly developing new retail format, have a significant impact on both consumer convenience and the brand's commercial profits and logistics costs. With the rise of online food delivery and other services, convenience stores need to optimize their location selection and spatial layout to meet modern consumers' demands and enhance market competitiveness. This paper takes the Everyday Chain as the research subject, analyzing its spatial distribution and influencing factors in the main urban area of Xi'an. A Maximum Coverage Location Model is adopted, combined with deep reinforcement learning and genetic algorithms for optimization. The study results indicate that deep reinforcement learning outperforms genetic algorithms regarding solution efficiency and coverage performance, offering a new approach and reference for convenience store location optimization. This can better enhance the rationality of service layout and the market competitiveness of convenience stores.

关键词： Deep Reinforcement Learning Convenience Stores Location optimization Spatial Analysis Geospatial optimization Everyday Chain

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HPC formulations of optimization algorithms for tensor completion

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PARALLEL COMPUTING 2018年 74卷 99-117页

作者： Smith, Shaden Park, Jongsoo Karypis, George Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA Facebook Menlo Pk CA USA

Tensor completion is a powerful tool used to estimate or recover missing values in multi way data. It has seen great success in domains such as product recommendation and healthcare. Tensor completion is most often accomplished via low-rank sparse tensor factorization, a computationally expensive non-convex optimization problem which has only recently been studied in the context of parallel computing. In this work, we study three optimization algorithms that have been successfully applied to tensor completion: alternating least squares (ALS), stochastic gradient descent (SGD), and coordinate descent (CCD++). We explore opportunities for parallelism on shared- and distributed-memory systems and address challenges such as memory- and operation-efficiency, load balance, cache locality, and communication. Among our advancements are a communication efficient CCD++ algorithm, an ALS algorithm rich in level-3 BLAS routines, and an SGD algorithm which combines stratification with asynchronous communication. Furthermore, we show that introducing randomization during ALS and CCD++ can accelerate convergence. We evaluate our parallel formulations on a variety of real datasets on a modern supercomputer and demonstrate speedups through 16384 cores. These improvements reduce time-to-solution from hours to seconds on real-world datasets. We show that after our optimizations, ALS is advantageous on parallel systems of small-to-moderate scale, while both ALS and CCD++ provide the lowest time-to-solution on large-scale distributed systems. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Sparse tensor Tensor completion Unstructured algorithm Machine learning Factorization Recommender system

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Nondifferentiable optimization: Motivations and applications 1

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丛书名： Lecture Notes in Economics and Mathematical Systems

1000年

作者： Vladimir F. Demyanov Diethard Pallaschke

ISBN: (数字)9783662126035

ISBN: (纸本)9783540159797

The international Institute for Applied Systems Analysis (IIASA) in Laxenburg, Austria, has been involved in research on nondifferentiable optimization since 1976. IIASA-based East-West cooperation in this field has been very productive, leading to many important theoretical, algorithmic and applied results. Nondifferentiable optimi zation has now become a recognized and rapidly developing branch of mathematical programming. To continue this tradition, and to review recent developments in this field, IIASA held a workshop on Nondifferentiable optimization in Sopron (Hungary) in September 1964. The aims of the workshop were: 1. To discuss the state-of-the-art of nondifferentiable optimization (NDO), its origins and motivation; 2. To compare-various algorithms; 3. To evaluate existing mathematical approaches, their applications and potential; 4. To extend and deepen industrial and other applications of NDO. The following topics were considered in separate sessions: General motivation for research in NDO: nondifferentiability in applied problems, nondifferentiable mathematical models. Numerical methods for solving nondifferentiable optimization problems, numerical experiments, comparisons and software. Nondifferentiable analysis: various generalizations of the concept of subdifferen tials. Industrial and other applications. This volume contains selected papers presented at the workshop. It is divided into four sections, based on the above topics: I. Concepts in Nonsmooth Analysis II. Multicriteria optimization and Control Theory III. algorithms and optimization Methods IV. Stochastic Programming and applications We would like to thank the international Institute for Applied Systems Analysis, particularly Prof. V. Kaftanov and Prof. A.B. Kurzhanski, for their support inorganiz ing this meeting.

关键词： Economic Theory/Quantitative Economics/Mathematical Methods

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A Personnel Rating optimization Algorithm 11

A Personnel Rating Optimization Algorithm

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11th IEEE international Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and applications (IDAACS)

作者： Yuan, Jianyu Yan, Chunyan Ye, Zhiwei Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781665426053

In view of the enterprise personnel management (EPM), a novel nature-inspired optimization algorithm called personnel rating optimization (PRO) is proposed in this paper. The design of PRO mimics the workflow of management activities from EPM to achieve the aim for bolstering the team's competence level. Built on the mechanisms of PRO, including personal promotion, swarm interaction, and coordinated management, it could quickly lock onto the solution region in high-dimensional space. In this study, we analyzed the essential similarities between PRO and EMP about management strategies, then presented the detailed procedures and mathematical model applied in PRO. A set of various numerical benchmark functions have been conducted on PRO and some other state-of-art natural computation algorithms including whale optimization algorithm ( WOA), differential evolution algorithm (DE), genetic algorithm (GA) and particle swarm optimization (PSO). Experimental results show that PRO has high precision and good stability, and the premature phenomenon is effectively restrained.

关键词： Personnel rating optimization algorithm Evolutionary algorithms global numerical optimization

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2015 workshop on Engineering applications - international Congress on Engineering, WEA 2015

2015 Workshop on Engineering Applications - International Co...

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workshop on Engineering applications - international Congress on Engineering, WEA 2015

ISBN: (纸本)9781509002283

The proceedings contain 39 papers. The topics discussed include: path tracking control for an autonomous underwater vehicle;failure modeling in a WDM optical burst switched network;estimation of longitudinal and lateral tire forces in a commercial vehicle;comparative analysis between voltage unbalance definitions;a mixed integer optimization model to design a selective collection routing problem for domestic solid waste;local and global path generation for autonomous vehicles using splines;instrumentation and control of a DC motor through the UBI dots platform;review of NTC 2050 and NFPA 70 on their section dedicated to equipments for electrical vehicles recharge systems;configuration of propagation models for computational simulation of different telecommunications services;prototypes selection based on similarity relations for classification problems;communications system for a soil monitoring network in Valle del Cauca, Colombia;and three-phase multilevel inverter with selective harmonic elimination.

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optimization of antenna designs using the FDTD simulation method enhanced with the subcell modeling technique

Optimization of antenna designs using the FDTD simulation me...

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3rd IEEE international workshop on Antenna Technology

作者： Benkler, S. Chavannes, N. Kuster, N. ETH Fdn Res Informat Technol Soc ITIS Gloriastr 35 CH-8092 Zurich Switzerland Schmid & Partner Engn AG CH-8004 Zurich Switzerland

ISBN: (纸本)9781424410873

This article demonstrates the capability of using the finite-differences time-domain (FDTD) method as simulation tool for optimizing the design of an antenna. The FDTD simulation method is locally enhanced with subcell modeling technique, which incorporates a-priori known field behavior in (1) curved material interfaces and (2) strong field gradients near sharp metal edges. Combining the FDTD subcell modeling technique with a FDTD simulation hardware acceleration card enables the efficient optimization of several parameters based on genetic algorithms.

关键词： Antennas

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Enhancement of radiograph images based on chaos optimization

Enhancement of radiograph images based on chaos optimization

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2009 international workshop on Intelligent Systems and applications, ISA 2009

作者： Sun, Yinjie College of computer and information technology Henan normal university Xinxiang China

ISBN: (纸本)9781424438945

How to select the parameters of high-frequency emphasis filtering (HFEF) in order to enhance radiograph images, this paper presents an enhancing method based on chaos optimization algorithms (COA). Based on the properties of ergodicity, stochastic property and regularity of chaos, the chaos optimization method can get global solution with low computational load. Firstly, an X-ray image is sharpened with HFEF, at the same time, the controllable and improved COA can optimize the parameters so that the optimal clarity of the image may be got, then the contrast of it is enhanced with histogram equalization to visually enhance the medical radiograph images to solve the questions: blurring and dark nature, which radiograph images generally tend to have. Experimental results show the proposed approach is effective and gets competitive visual effects. © 2009 IEEE.

关键词： Image enhancement

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Shape optimization of multi-band antennas using the coupling between microgenetic algorithms and TLM method

Shape optimization of multi-band antennas using the coupling...

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5th international workshop on Computational Electromagnetics in the Time-Domain

作者： Chu, HS Dubard, JL Staraj, R Univ Nice Sophia Antipolis CNRS UMR 6071 Lab Elect Antennes & Telecommun F-06560 Valbonne France

This paper presents a new combination between transmission line matrix method (TLM) and microgenetic algorithm (muGA). This coupling is used to design patch shapes of microstrip and planar inverted-F antennas (PIFA) for broad-band or multi-band applications. Measured results of the muGA/TLM optimized designs show good agreement with TLM simulation. Copyright (C) 2004 John Wiley Sons, Ltd.

关键词： genetic algorithms TLM method microstrip antenna PIFA antenna

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Local Search algorithms for Solving the Combinatorial optimization and Constraint Satisfaction Problems 6th

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6th international workshop on Soft Computing applications (SOFA)

作者： Kilani, Y. Alsarhan, A. Bsoul, M. Otoom, A. F. Hashemite Univ Fac Prince Al Hussein Bin Abdullah II Informat Te Dept Comp Informat Syst Zarqa Jordan Hashemite Univ Fac Prince Al Hussein Bin Abdullah II Informat Te Dept Comp Sci & Applicat Zarqa Jordan Hashemite Univ Fac Prince Al Hussein Bin Abdullah II Informat Te Dept Software Engn Zarqa Jordan

ISBN: (纸本)9783319182964

Local search is a metaheuristic for solving computationally hard optimization problems. In the past three decades, local search has grown from a simple heuristic idea into a mature field of research in combinatorial optimization that is attracting ever-increasing attention. It is still the method of choice for NP-hard problems as it provides a robust approach for obtaining high-quality solutions to problems of a realistic size in reasonable time. optimization problems such as the shortest path, the traveling salesman, pin packing, and the Knapsack problems. Local search techniques have been successful in solving large and tight constraint satisfaction problems. Local search algorithms turn out to be effective in solving many constraint satisfaction problems. This chapter gives an introduction to the local search algorithms, the optimization and the constraint satisfaction problems, and the local search methods used to solve them.

关键词： Local search Introduction Combinatorial optimization problems Constraint satisfaction problems

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Automatic differentiation applied to convex optimization

Automatic differentiation applied to convex optimization

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2nd international workshop on Computational Differentiation - Techniques, applications, and Tools

作者： Hassold, E Galligo, A

ISBN: (纸本)0898713854

In several applications, one is led to minimize a nonlinear function f that is differentiable or at least admits gradients almost everywhere. In this paper, we outline optimization algorithms that rely on explicit computations of gradients or limits of gradients, using specific automatic differentiation techniques. We consider functions represented by Fortran programs. We suppose that singularities are consequences either of ''branching'' operations (absolute value, max, conditional structures with simple tests) or of classical elementary functions (square root when computing an Euclidean norm), which generate kinks where f admits a directional derivative in any direction. We present algorithms and implementations on top of the automatic differentiation system Odyssee to compute directional derivatives and limits of gradients that allow descriptions of normal cones. Together with the input Fortran code, this is used by our optimization library Odymin to minimize f. Finally, we discuss the capability, efficiency, and extensibility of our approach. We compare the number of calls required by different strategies for a classical example.

关键词： optimization nonsmooth optimization directional Taylor expansion convex optimization bundle methods Odyssee

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