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Mixed-Binary Problems Optimized with Fast Discrete Solver 25th

Mixed-Binary Problems Optimized with Fast Discrete Solver

25th European Conference on Evolutionary Computation in Combinatorial optimization, EvoCOP 2025 Held as Part of EvoStar 2025

作者： Kötzing, Timo Radhakrishnan, Aishwarya Hasso Plattner Institute University of Potsdam Potsdam Germany

ISBN: (纸本)9783031868481

Tackling optimization in mixed domains (continuous and discrete decision variables) has recently gained attention, causing the development of various extensions of continuous optimization algorithms. In order to more accurately address the combinatorial nature of the discrete aspects of the search space, we go a different way and combine two algorithms, one for each aspect of the search space. Focusing, in the discrete part, on binary search spaces, we use Population-Based Incremental Learning (PBIL) for discrete optimization. We combine this algorithm with the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) to address the continuous part. We compare our CMA-ES-PBIL with two leading variants of CMA-ES from the literature: CMA-ES with Margin (CMA-ESwM) and CMA-ES with Probability Distribution Model (CMA-ES-PDM). We conduct run time analysis on some separable and a non-separable benchmark functions and show that our hybrid algorithm significantly outperforms both CMA-ESwM and CMA-ES-PDM. Our results also show that the binary part is optimized much earlier than the continuous part, raising the question: Is run time evaluation the right measure to analyze mixed-binary algorithms? © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： optimization algorithms

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A New Algorithm for Computing α-Capacity 18

A New Algorithm for Computing α-Capacity

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18th International Symposium on Information Theory and Its Applications, ISITA 2024

作者： Kamatsuka, Akira Kazama, Koki Yoshida, Takahiro Shonan Institute of Technology Japan Nihon University Japan

ISBN: (纸本)9784885523519

The problem of computing α-capacity for α > 1 is equivalent to that of computing the correct decoding exponent. Various algorithms for computing them have been proposed, such as Arimoto and Jitsumatsu-Oohama algorithm. In this study, we propose a novel alternating optimization algorithm for computing the α -capacity for α > 1 based on a variational characterization of the Augustin-Csiszár mutual information. A comparison of the convergence performance of these algorithms is demonstrated through numerical examples. © 2024 The Institute of Electronics, Information and Communication Engineers.

关键词： optimization algorithms

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Constraint Preconditioning and Parameter Selection for a First-Order Primal-Dual Method applied to Model Predictive Control 63

Constraint Preconditioning and Parameter Selection for a Fir...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Chari, Govind M. Yu, Yue Acimese, Behcet University of Washington Department of Aeronautics & Astronautics SeattleWA98195 United States University of Minnesota Twin Cities Department of Aerospace Engineering and Mechanics MinneapolisMN55455 United States

ISBN: (纸本)9798350316339

Many techniques for real-time trajectory optimization and control require the solution of optimization problems at high frequencies. However, ill-conditioning in the optimization problem can significantly reduce the speed of firstorder primal-dual optimization algorithms. We introduce a preconditioning technique and step-size heuristic for Proportional-Integral Projected Gradient (PIPG), a first-order primal-dual algorithm. The preconditioning technique, based on the QR factorization, aims to reduce the condition number of the KKT matrix associated with the optimization problem. Our step-size selection heuristic chooses step-sizes to minimize the upper bound on the convergence of the primal-dual gap for the optimization problem. These algorithms are tested on two model predictive control problem examples and show a solve-time reduction of at least 3.6 x. © 2024 IEEE.

关键词： optimization algorithms

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Gradient-Guided Local Search for IGD/IGDPlus Subset Selection

Gradient-Guided Local Search for IGD/IGDPlus Subset Selectio...

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2024 Genetic and Evolutionary Computation Conference, GECCO 2024

作者： Nan, Yang Ishibuchi, Hisao Shu, Tianye Shang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen China

ISBN: (纸本)9798400704949

Subset selection is always a hot topic in the community of evolutionary multi-objective optimization (EMO) since it is used in mating selection, environmental selection, and final selection. In the first two scenarios, the task of subset selection algorithms is to select a subset from a small candidate set (e.g., population). However, in the last scenario, it is to select a subset from an unbounded archive with all non-dominated solutions examined during the evolutionary process. Most existing subset selection algorithms aim to improve the hypervolume of subsets (i.e., hypervolume subset selection) selected from the archive. However, only a few researchers work on the IGD and IGD+ (two well-known indicators) subset selection in the last scenario. In this paper, we propose a gradient-guided local search algorithm for IGD/IGD+ subset selection problems. The experimental results show that the proposed algorithm is much faster than the existing lazy greedy inclusion IGD/IGD+ subset selection algorithm, and the quality of the selected subsets is competitive with that selected by the existing greedy algorithms. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： optimization algorithms

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The Moth-Flame optimization Algorithm of Abrasive Water Jet Cutting Process

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Procedia Computer Science 2024年 246卷 2912-2921页

作者： Elżbieta Kawecka Piotr Puzio The Jacob of Paradies University Faculty of Technology Teatralna 25 66-400 Gorzów Wielkopolski Poland

The article presents the possibility of using the Moth-Flame optimization (MFO) algorithm for Abrasive Water Jet machining (AWJ) of structural steel materials. In order to carry out the optimization, an original program was written in Python programming language. In turn, for this optimization process the objective function was determined using the Response Surface Methodology (RSM). Then, a set of control parameters was determined at which the value of the objective function reaches an extreme value. The optimal value calculated based on the Moth-Flame optimization algorithm was compared with the value of the best effect determined experimentally.

关键词： optimization algorithms meta-heuristic algorithms Moth-Flame optimization algorithm Abrasive Water Jet (AWJ)

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ADMM-Tracking Gradient for Distributed optimization over Asynchronous and Unreliable Networks

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IEEE Transactions on Automatic Control 2025年

作者： Carnevale, Guido Bastianello, Nicola Notarstefano, Giuseppe Carli, Ruggero Alma Mater Studiorum - Universita' di Bologna Department of Electrical Electronic and Information Engineering Bologna Italy Kth Royal Institute of Technology School of Electrical Engineering and Computer Science and Digital Futures Stockholm Sweden University of Padova Department of Information Engineering Via G. Gradenigo 6/B Padova35131 Italy

In this paper, we propose a novel distributed algorithm for consensus optimization over networks and a robust extension tailored to deal with asynchronous agents and packet losses. Indeed, to robustly achieve dynamic consensus on the solution estimates and the global descent direction, we embed in our algorithms a distributed implementation of the Alternating Direction Method of Multipliers (ADMM). Such a mechanism is suitably interlaced with a local proportional action steering each agent estimate to the solution of the original consensus optimization problem. First, in the case of ideal networks, by using tools from system theory, we prove the linear convergence of the scheme with strongly convex costs. Then, by exploiting the averaging theory, we extend such a first result to prove that the robust extension of our method preserves linear convergence in the case of asynchronous agents and packet losses. Further, by using the notion of Input-to-State Stability, we also guarantee the robustness of the schemes with respect to additional, generic errors affecting the agents' updates. Finally, some numerical simulations confirm our theoretical findings and compare our algorithms with other distributed schemes in terms of speed and robustness. © 2025 IEEE.

关键词： optimization algorithms

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Distributed Event-Triggered Bandit Convex optimization With Time-Varying Constraints

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IEEE Transactions on Control of Network Systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： optimization algorithms

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How rotation invariant algorithms are fooled by noise on sparse targets 36

How rotation invariant algorithms are fooled by noise on spa...

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36th International Conference on Algorithmic Learning Theory, ALT 2025

作者： Warmuth, Manfred K. Kotlowski, Wojciech Jones, Matt Amid, Ehsan Google Inc. United States Institute of Computing Science Poznań University of Technology Poznań Poland University of Colorado BoulderCO United States

It is well known that rotation invariant algorithms are sub-optimal for learning sparse linear problems, when the number of examples is below the input dimension. This includes any gradient descent trained neural net with a fully-connected input layer initialized with a rotationally symmetric distribution. The simplest sparse problem is learning a single feature out of d features. In that case the classification error or regression loss of rotation invariant algorithms grows with 1 −n/d, where n is the number of examples seen. These lower bounds become vacuous when the number of examples n reaches the dimension d. After d examples, the gradient space has full rank and any weight vector can be expressed, including the unit vector that determines the target feature. In this work, we show that when noise is added to this sparse linear problem, rotation invariant algorithms are still sub-optimal after seeing d or more examples. We prove this via a lower bound for the Bayes optimal algorithm on a rotationally symmetrized problem. We then prove much better upper bounds on the same problem for a large variety of algorithms that are non-invariant by rotations. Finally, we analyze the gradient flow trajectories of many standard optimization algorithms (such as AdaGrad) on the same noisy feature learning problem, and show how they veer away from the noisy sparse targets. We then contrast them with a group of non-rotation invariant algorithms that veer towards the sparse targets. We believe that the lower bounds method and trajectory categorization will be crucial for analyzing other families of algorithms with different classes of invariances. © 2025 M.K. Warmuth, W. Kotlowski, M. Jones & E. Amid.

关键词： optimization algorithms

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RFN: A Random-Feature Based Newton Method for Empirical Risk Minimization in Reproducing Kernel Hilbert Spaces

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 5308-5319页

作者： Chang, Ting-Jui Shahrampour, Shahin Northeastern Univ Dept Mech & Ind Engn Boston MA 02115 USA

In supervised learning using kernel methods, we often encounter a large-scale finite-sum minimization over a reproducing kernel Hilbert space (RKHS). Large-scale finite-sum problems can be solved using efficient variants of Newton method, where the Hessian is approximated via sub-samples of data. In RKHS, however, the dependence of the penalty function to kernel makes standard sub-sampling approaches inapplicable, since the gram matrix is not readily available in a low-rank form. In this paper, we observe that for this class of problems, one can naturally use kernel approximation to speed up the Newton method. Focusing on randomized features for kernel approximation, we provide a novel second-order algorithm that enjoys local superlinear convergence and global linear convergence (with high probability). We derive the theoretical lower bound for the number of random features required for the approximated Hessian to be close to the true Hessian in the norm sense. Our numerical experiments on real-world data verify the efficiency of our method compared to several benchmarks.

关键词： Newton method optimization algorithms risk minimization Hessian approximation random features

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Unconventional hydrocarbon resources: geological statistics, petrophysical characterization, and field development strategies

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2022年第6期12卷 1463-1488页

作者： Muther, Temoor Qureshi, Haris Ahmed Syed, Fahad Iqbal Aziz, Hassan Siyal, Amaar Dahaghi, Amirmasoud Kalantari Negahban, Shahin Univ Kansas Dept Chem & Petr Engn Lawrence KS 66045 USA Al Sadim Oil Serv Tripoli Libya Dawood Univ Engn & Technol Dept Petr & Gas Engn Karachi Pakistan Khalifa Univ Abu Dhabi U Arab Emirates

Hydrocarbons exist in abundant quantity beneath the earth's surface. These hydrocarbons are generally classified as conventional and unconventional hydrocarbons depending upon their nature, geology, and exploitation procedure. Since the conventional hydrocarbons are under the depletion phase, the unconventional hydrocarbons have been a major candidate for current and future hydrocarbon production. Additionally, investment and research have increased significantly for its exploitation. Having the shift toward unconventional hydrocarbons, this study reviews in depth the technical aspects of unconventional hydrocarbons. This review brings together all the important aspects of unconventional reservoirs in single literature. This review at first highlights the worldwide unconventional hydrocarbon resources, their technical concept, distribution, and future supplies. A portion of this study also discusses the resources of progressive unconventional hydrocarbon candidates. Apart from this, this review also highlights the geological aspects of different unconventional hydrocarbon resources including tight, shale, and coalbed methane. The petrophysical behavior of such assists including the response to well logs and the discussion of improved correlation for petrophysical analysis is a significant part of this detailed study. The variation in geology and petrophysics of unconventional resources with conventional resources are also presented. In addition, the latest technologies for producing unconventional hydrocarbons ranging from fractured wells to different fluid injections are discussed in this study. In the end, the latest machine learning and optimization techniques have been discussed that aids in the optimized field development planning of unconventional reservoirs.

关键词： Unconventional hydrocarbons Machine learning optimization algorithms Shale and tight resources Geology Petrophysics Coalbed methane Unconventional reservoir development

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