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Market graph construction using the performance measure of similarity 1

3rd International Conference on Network Analysis, Net 2013

作者： Glotov, Andrey A. Kalyagin, Valery A. Vizgunov, Arsenii N. Pardalos, Panos M. National Research University Higher School of Economics 25/12 Bolshaja Pecherskaja Ulitsa Nizhny Novgorod603155 Russia Laboratory of Algorithms and Technologies for Networks Analysis Department of Applied Mathematics and Informatics National Research University Higher School of Economics Nizhny Novgorod603155 Russia Department of Industrial and Systems Engineering Center for Applied Optimization University of Florida GainesvilleFL32608 United States Laboratory of Algorithms and Technologies for Networks Analysis National Research University Higher School of Economics Nizhny Novgorod603155 Russia

ISBN: (数字)9783319097589

ISBN: (纸本)9783319097572

The paper presents the description of the modification of the stock market graph model. Authors suggest a new similarity measure between stocks. Following the market graph model a vertex represents a stock. For each pair of stocks we calculate a number of weeks when both stocks were profitable simultaneously. This number is used as a weight of edge between two vertices. The structural properties of the graph constructed by means of the suggested measure can be used to evaluate potential profitability, performance of the stock market. The model was applied to Russian stock market and to US stock market for the time period from 2001 to 2011. The main result of the analysis is the description of the peculiarity of the Russian stock market comparing with the well-developed US stock market and observation that constructed US stock market graph follows the power law model. © Springer International Publishing Switzerland 2014.

关键词： Financial markets

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Approximating Geometric Knapsack via L-packings

Approximating Geometric Knapsack via L-packings

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作者： Gálvez, Waldo Grandoni, Fabrizio Ingala, Salvatore Heydrich, Sandy Khan, Arindam Wiese, Andreas Department of Computer Science Technical University of Munich Garching Germany Chair of Approximation Algorithms IDSIA USI-SUPSI via la Santa 1 Lugano-Viganello6962 Switzerland Department of Optimization Fraunhofer Institute for Industrial Mathematics Kaiserslautern Germany Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Industrial Engineering Center for Mathematical Modeling Universidad de Chile Chile

We study the two-dimensional geometric knapsack problem, in which we are given a set of n axis-aligned rectangular items, each one with an associated profit, and an axis-aligned square knapsack. The goal is to find a (non-overlapping) packing of a maximum profit subset of items inside the knapsack (without rotating items). The best-known polynomial-time approximation factor for this problem (even just in the cardinality case) is 2+ϵ [Jansen and Zhang, SODA 2004]. In this article we present a polynomial-time 17/9+ϵ © 2021 Association for Computing Machinery.

关键词： Profitability

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A hybrid metaheuristic for routing on multicast networks 1

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3rd International Conference on Network Analysis, Net 2013

作者： Oliveira, Carlos A. S. Pardalos, Panos M. Quantitative Research Dept. F-Squared Inc PrincetonNJ United States Department of Industrial and Systems Engineering Center for Applied Optimization University of Florida GainesvilleFL32608 United States Laboratory of Algorithms and Technologies for Networks Analysis National Research University Higher School of Economics Nizhny Novgorod603155 Russia

ISBN: (数字)9783319097589

ISBN: (纸本)9783319097572

Multicast routing systems have the objective of simultaneously transferring data to multiple destination nodes while using a single "push" operation. This leads to cost savings associated with reduced bandwidth utilization, which results from a decrease in data duplication across network links. An important problem on multicast networks, known as the delay constrained multicast routing problem (DCMRP), asks for the determination of an optimal route for packet transfers between members of a multicast group. Several heuristics have been proposed in the last few years to solve the DCMRP, which is of great interest for telecommunication engineers. In this paper we propose a novel, hybrid metaheuristic approach for the DCMRP, where a greedy randomized adaptive search procedure is used along with variable neighborhood search algorithm to find near optimal solutions. Computational experiments show that the proposed technique provides superior solution quality, while it is also efficient in terms of the use of computational resources. © Springer International Publishing Switzerland 2014.

关键词： optimization

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Biased random-key genetic algorithm for linearly-constrained global optimization

Biased random-key genetic algorithm for linearly-constrained...

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15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013

作者： Silva, Ricardo M.A. Resende, Mauricio G.C. Pardalos, Panos M. Facó, João L. Centro de Informática Universidade Federal de Pernambuco Recife PE Brazil Algorithms and Optimization Research Dep. NJ United States Department of Industrial and Systems Engineering University of Florida FL United States Instituto de Matemática Universidade Federal do Rio de Janeiro RJ Brazil

In this paper, we propose a biased random key genetic algo- rithm for finding approximate solutions for bound-constrained continuous global optimization problems subject to linear constraints. Experimental results ill... 详细信息

ISBN: (纸本)9781450319645

关键词： Genetic algorithms

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The improved genetic algorithms for digital image correlation method

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Chinese Optics Letters 2004年第10期2卷 574-577页

作者：唐晨刘铭闫海青张桂敏陈湛青 Department of Applied Physics University of Tianjin Tianjin 300072 Department of Mechanical Engineering and Automation Harbin Institute of Technology Harbin 150001e present a global optimization method called the genetic algorithms (GAs) for digital image/speckle correlation (DISC). The new algorithms do not involve reasonable initial guess of displacement and deformation gradient and the calculation of second-order spatial derivatives of the digital images which are important challenges in practical implementation of DISC. The performance of a GA depends largely on the selection of the genetic operators. We test various operators and propose optimal operators. The algorithms are then verified using simulated images and experimental speckle images.

We present a global optimization method, called the genetic algorithms (GAs), for digital image/speckle correlation (DISC). The new algorithms do not involve reasonable initial guess of displacement and deformation gradient and the calculation of second-order spatial derivatives of the digital images, which are important challenges in practical implementation of DISC. The performance of a GA depends largely on the selection of the genetic operators. We test various operators and propose optimal operators. The algorithms are then verified using simulated images and experimental speckle images.

关键词： gene ERR The improved genetic algorithms for digital image correlation method RGA body DISC

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Neural networks in day-ahead electricity price forecasting: single vs. multiple outputs

arXiv

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arXiv 2020年

作者： Marcjasz, Grzegorz Lago, Jesus Weron, Rafal Department of Operations Research Wroclaw University of Science and Technology Wroclaw Poland Delft Center for Systems and Control Delft University of Technology Delft Netherlands Algorithms Modeling and Optimization Energyville Genk Belgium Mol Belgium

Recent advancements in the fields of artificial intelligence and machine learning methods resulted in a significant increase of their popularity in the literature, including electricity price forecasting. Said methods cover a very broad spectrum, from decision trees, through random forests to various artificial neural network models and hybrid approaches. In electricity price forecasting, neural networks are the most popular machine learning method as they provide a non-linear counterpart for well-tested linear regression models. Their application, however, is not straightforward, with multiple implementation factors to consider. One of such factors is the network’s structure. This paper provides a comprehensive comparison of two most common structures when using the deep neural networks – one that focuses on each hour of the day separately, and one that reflects the daily auction structure and models vectors of the prices. The results show a significant accuracy advantage of using the latter, confirmed on data from five distinct power exchanges. Copyright © 2020, The Authors. All rights reserved.

关键词： Risk management

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Dynamic online optimization in the context of smart manufacturing: an overview

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Procedia Computer Science 2021年 180卷 988-995页

作者： Viktoria A. Hauder Andreas Beham Stefan Wagner Karl F. Doerner Michael Affenzeller Josef Ressel Center for Adaptive Optimization in Dynamic Environments University of Applied Sciences Upper AustriaSoftwarepark 13 4232 Hagenberg Austria Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Softwarepark 11 4232 Hagenberg Austria Josef Ressel Center for Adaptive Optimization in Dynamic Environments Department of Business Decisions and AnalyticsUniversity of Vienna Oskar-Morgenstern-Platz 1 1090 Vienna Austria

Solving manufacturing optimization problems in the context of intelligent production involves the consideration of continuously changing events of the respective enterprise environment in real time. Smart solution methods are needed which are able to cope with such necessary reactions to uncertainty and dynamics. In general, this field of research belongs to the topic of dynamic optimization. However, investigating the relevant literature reveals the broad range of this research area. In addition to real time, i.e. online optimization it contains a large number of other (dynamic) sectors. After differentiating dynamic online optimization from other research domains of dynamic optimization, the aim of this work is (1) to show in which streams and problem fields it has already been investigated, and (2) which different approaches to categorize online optimization problems are known so far. As a result, an overview of the state of the art concerning the occurence and existing categorizations of online optimization problems in the context of smart manufacturing is given, demonstrating ambiguities in the language used and in the categorization efforts for this optimization problem and therefore motivating further research efforts on a comprehensive integration of the findings of different streams in this area.

关键词： Dynamic online optimization uncertainty real time smart manufacturing categorization

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The effectiveness of Lloyd-type methods for the k-means problem

The effectiveness of Lloyd-type methods for the k-means prob...

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作者： Ostrovsky, Rafail Rabani, Yuval Schulman, Leonard J. Swamy, Chaitanya Department of Computer Science and Department of Mathematics UCLA Box 951596 3732D BH Los Angeles CA 90095-1596 United States School of Computer Science and Engineering and the Center of Excellence on Algorithms Hebrew University of Jerusalem Jerusalem 91904 Israel Caltech 1200 East California Blvd. Pasadena CA 91125 United States Department of Combinatorics and Optimization Faculty of Mathematics MC 4033 University of Waterloo 200 University Avenue West Waterloo ON N2L 3G1 Canada

We investigate variants of Lloyd's heuristic for clustering high-dimensional data in an attempt to explain its popularity (a half century after its introduction) among practitioners, and in order to suggest improvements in its *** propose and justify a clusterability criterion for data *** present variants of Lloyd's heuristic that quickly lead to provably near-optimal clustering solutions when applied to well-clusterable instances. This is the first performance guarantee for a variant of Lloyd's heuristic. The provision of a guarantee on output quality does not come at the expense of speed: some of our algorithms are candidates for being faster in practice than currently used variants of Lloyd's method. In addition, our other algorithms are faster on well-clusterable instances than recently proposed approximation algorithms, while maintaining similar guarantees on clustering quality. Our main algorithmic contribution is a novel probabilistic seeding process for the starting configuration of a Lloyd-type iteration. © 2012 ACM.

关键词： Approximation algorithms

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Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark

arXiv

引用

arXiv 2020年

作者： Lago, Jesus Marcjasz, Grzegorz De Schutter, Bart Weron, Rafal Delft Center for Systems and Control Delft University of Technology Delft Netherlands Algorithms Modeling and Optimization Energyville Genk Belgium Mol Belgium Department of Operations Research and Business Intelligence Wroclaw University of Science and Technology Wroclaw Poland

While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The proposed new methods are rarely benchmarked against well established and well performing simpler models, the accuracy metrics are sometimes inadequate and testing the significance of differences in predictive performance is seldom conducted. Consequently, it is not clear which methods perform well nor what are the best practices when forecasting electricity prices. In this paper, we tackle these issues by comparing state-of-the-art statistical and deep learning methods across multiple years and markets, and by putting forward a set of best practices. In addition, we make available the considered datasets, forecasts of the state-of-the-art models, and a specifically designed python toolbox, so that new algorithms can be rigorously evaluated in future studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Power markets

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Time complexity analysis of randomized search heuristics for the dynamic graph coloring problem

arXiv

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arXiv 2021年

作者： Bossek, Jakob Neumann, Frank Peng, Pan Sudholt, Dirk Statistics and Optimization University of Münster Münster Germany School of Computer Science The University of Adelaide Adelaide Australia Department of Computer Science University of Sheffield Sheffield United Kingdom Algorithms for Intelligent Systems University of Passau Passau Germany

We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical vertex coloring problem on graphs and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. The (1+1) Evolutionary Algorithm and RLS operate in a setting where the number of colors is bounded and we are minimizing the number of conflicts. Iterated local search algorithms use an unbounded color palette and aim to use the smallest colors and, consequently, the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i.e., starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. We further show that tailoring mutation operators to parts of the graph where changes have occurred can significantly reduce the expected reoptimization time. In most settings the expected reoptimization time for such tailored algorithms is linear in the number of added edges. However, tailored algorithms cannot prevent exponential times in settings where the original algorithm is inefficient. Copyright © 2021, The Authors. All rights reserved.

关键词： Heuristic algorithms

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