Relevant bibliographies by topics / Zero-Order optimization

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Zero-Order optimization'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Zero-Order optimization"

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Bychkov, G. K., D. M. Dvinskikh, A. V. Antsiferova, A. V. Gasnikov, and A. V. Lobanov. "Accelerated Zero-Order SGD under High-Order Smoothness and Overparameterized Regime." Nelineinaya Dinamika 20, no. 5 (2024): 759–88. https://doi.org/10.20537/nd241209.

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We present a novel gradient-free algorithm to solve a convex stochastic optimization problem, such as those encountered in medicine, physics, and machine learning (e.g., the adversarial multi-armed bandit problem), where the objective function can only be computed through numerical simulation, either as the result of a real experiment or as feedback given by the function evaluations from an adversary. Thus, we suppose that only black-box access to the function values of the objective is available, possibly corrupted by adversarial noise: deterministic or stochastic. The noisy setup can arise n
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Veprikov, A. S., E. D. Petrov, G. V. Evseev, and A. N. Beznosikov. "Zero Order Algorithm for Decentralized Optimization Problems." Doklady Mathematics 110, S1 (2024): S261—S277. https://doi.org/10.1134/s1064562424602336.

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Abstract In this paper we consider a distributed optimization problem in the black-box formulation. This means that the target function f is decomposed into the sum of $$n$$ functions $${{f}_{i}}$$ , where $$n$$ is the number of workers, it is assumed that each worker has access only to the zero-order noisy oracle, i.e., only to the values of $${{f}_{i}}(x)$$ with added noise. In this paper, we propose a new method based on the state-of-the-art distributed optimization algorithm MARINA. In particular, the following modifications are made to solve the problem in the black-box formulation: (i) w
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Lu, Yanbo, Huimin Gao, Yi Zhang, and Yong Xu. "FAFedZO: Faster Zero-Order Adaptive Federated Learning Algorithm." Electronics 14, no. 7 (2025): 1452. https://doi.org/10.3390/electronics14071452.

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Federated learning represents a newly emerging methodology in the field of machine learning that enables distributed agents to collaboratively learn a centralized model without sharing their raw data. Some scholars have already proposed many first-order algorithms and second-order algorithms for federated learning to reduce communication costs and speed up convergence. However, these algorithms generally rely on gradient or Hessian information, and we find it difficult to solve such federated optimization problems when the analytical expression of the loss function is not available, that is, w
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Shao, Yingzhao, Yanxin Chen, Pengfei Yang, and Fei Cheng. "EnNet: Enhanced Interactive Information Network with Zero-Order Optimization." Sensors 24, no. 19 (2024): 6361. http://dx.doi.org/10.3390/s24196361.

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Interactive image segmentation extremely accelerates the generation of high-quality annotation image datasets, which are the pillars of the applications of deep learning. However, these methods suffer from the insignificance of interaction information and excessively high optimization costs, resulting in unexpected segmentation outcomes and increased computational burden. To address these issues, this paper focuses on interactive information mining from the network architecture and optimization procedure. In terms of network architecture, the issue mentioned above arises from two perspectives:
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Kolosnitsyn, Anton, Oleg Khamisov, Eugene Semenkin, and Vladimir Nelyub. "Application of the Parabola Method in Nonconvex Optimization." Algorithms 17, no. 3 (2024): 107. http://dx.doi.org/10.3390/a17030107.

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We consider the Golden Section and Parabola Methods for solving univariate optimization problems. For multivariate problems, we use these methods as line search procedures in combination with well-known zero-order methods such as the coordinate descent method, the Hooke and Jeeves method, and the Rosenbrock method. A comprehensive numerical comparison of the obtained versions of zero-order methods is given in the present work. The set of test problems includes nonconvex functions with a large number of local and global optimum points. Zero-order methods combined with the Parabola method demons
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Lazari, Alexandru. "Optimization of Zero-Order Markov Processes with Final Sequence of States." Universal Journal of Applied Mathematics 1, no. 3 (2013): 198–206. http://dx.doi.org/10.13189/ujam.2013.010307.

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Jiang, Peng Yu, Zhu Ming Lin, Jin Xu, and Jian Qun Sun. "A Particle Swarm Optimization Algorithm for Minimizing Weight of the Composite Box Structure." Advanced Materials Research 430-432 (January 2012): 470–75. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.470.

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The particle swarm optimization was applied to the optimum design of the composite box structure.The total weight of the structure was taken as the objective function.A technique of applying PSO integrated with general finite element code was developed for the optimization.Optimization was also conducted using zero-order method included in ANSYS and a comparison was made between zero-order method and PSO.Results demonstrate that PSO can find the global optimal design with higher efficiency regardless of the initial designs.for zero-order method the optimum solution is worst than the result of
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Kannan, B. K., and S. N. Kramer. "An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design." Journal of Mechanical Design 116, no. 2 (1994): 405–11. http://dx.doi.org/10.1115/1.2919393.

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An algorithm for solving nonlinear optimization problems involving discrete, integer, zero-one, and continuous variables is presented. The augmented Lagrange multiplier method combined with Powell’s method and Fletcher and Reeves Conjugate Gradient method are used to solve the optimization problem where penalties are imposed on the constraints for integer/discrete violations. The use of zero-one variables as a tool for conceptual design optimization is also described with an example. Several case studies have been presented to illustrate the practical use of this algorithm. The results obtaine
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Poveda, Jorge I., and Na Li. "Robust hybrid zero-order optimization algorithms with acceleration via averaging in time." Automatica 123 (January 2021): 109361. http://dx.doi.org/10.1016/j.automatica.2020.109361.

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Jha, Ishan, and Krishna K. Pathak. "Fuzzy-based integrated zero-order shape optimization of steel–concrete–steel sandwich beams." Current Science 121, no. 7 (2021): 941. http://dx.doi.org/10.18520/cs/v121/i7/941-949.

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Dissertations / Theses on the topic "Zero-Order optimization"

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Mhanna, Elissa. "Beyond gradients : zero-order approaches to optimization and learning in multi-agent environments." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG123.

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L'essor des dispositifs connectés et des données qu'ils génèrent a stimulé le développement d'applications à grande échelle. Ces dispositifs forment des réseaux distribués avec un traitement de données décentralisé. À mesure que leur nombre augmente, des défis comme la surcharge de communication et les coûts computationnels se présentent, nécessitant des méthodes d'optimisation adaptées à des contraintes de ressources strictes, surtout lorsque les dérivées sont coûteuses ou indisponibles. Cette thèse se concentre sur les méthodes d'optimisation sans dérivées, qui sont idéales quand les dérivée
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Akhavanfoomani, Aria. "Derivative-free stochastic optimization, online learning and fairness." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG001.

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Dans cette thèse, nous étudions d'abord le problème de l'optimisation d'ordre zéro dans le cadre actif pour des fonctions lisses et trois classes différentes de fonctions : i) les fonctions qui satisfont la condition de Polyak-Łojasiewicz, ii) les fonctions fortement convexes, et iii) la classe plus large des fonctions non convexes fortement lisses.De plus, nous proposons un nouvel algorithme basé sur la randomisation de type l1, et nous étudions ses propriétés pour les fonctions convexes Lipschitz dans un cadre d'optimisation en ligne. Notre analyse est due à la dérivation d'une nouvelle inég
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Sanja, Lončar. "Negative Selection - An Absolute Measure of Arbitrary Algorithmic Order Execution." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 2017. https://www.cris.uns.ac.rs/record.jsf?recordId=104861&source=NDLTD&language=en.

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Algorithmic trading is an automated process of order execution on electronic stock markets. It can be applied to a broad range of financial instruments, and it is  characterized by a signicant investors' control over the execution of his/her orders, with the principal goal of finding the right balance between costs and risk of not (fully) executing an order. As the measurement of execution performance gives information whether best execution is achieved, a signicant number of diffeerent benchmarks is  used in practice. The most frequently used are price benchmarks, where some of
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Ali, Ghoddosian. "Improved zero order techniques for structural shape optimization using F.E.M." Thesis, 1998. http://localhost:8080/xmlui/handle/12345678/2949.

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Book chapters on the topic "Zero-Order optimization"

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Lobanov, Aleksandr, and Alexander Gasnikov. "Accelerated Zero-Order SGD Method for Solving the Black Box Optimization Problem Under “Overparametrization” Condition." In Optimization and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47859-8_6.

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Teunissen, P. "Zero Order Design: Generalized Inverses, Adjustment, the Datum Problem and S-Transformations." In Optimization and Design of Geodetic Networks. Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-70659-2_3.

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Lobanov, Aleksandr, Anton Anikin, Alexander Gasnikov, Alexander Gornov, and Sergey Chukanov. "Zero-Order Stochastic Conditional Gradient Sliding Method for Non-smooth Convex Optimization." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43257-6_8.

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Panteleev, A. V., and A. V. Lobanov. "Application of the Zero-Order Mini-Batch Optimization Method in the Tracking Control Problem." In SMART Automatics and Energy. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8759-4_59.

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Chen, Li-an, Mengqian Guo, Yongxin Jiang, Xi Chen, and Yiping Chen. "Identification Method of VT Ferroresonance Fault Based on Improved CEEMD and WOA-RF." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4856-6_10.

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Abstract In order to solve the shortcoming of the low accuracy of ferroresonance fault identification of voltage transformers (VT) by the traditional microcomputer harmonic suppression device, the T-test and variance contribution rate were introduced to form an improved complete ensemble empirical mode decomposition (ICEEMD) method, and an identification method of VT ferroresonance fault based on ICEEMD feature extraction, kernel principal components analysis (KPCA) feature reduction, and random forest (RF) construction optimized by the whale optimization algorithm (WOA) was proposed. Firstly,
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Schöneburg, Rodolfo, and Karl-Heinz Baumann. "What the Car Industry Can Do: Mercedes-Benz’ View." In The Vision Zero Handbook. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-76505-7_29.

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AbstractThe car industry faces some extreme challenges. Alongside the key task that we face of making individual transport climate-neutral, and alongside the new technological opportunities for communication and networking across our whole environment, it is clear that the user behavior of vehicle drivers and of all other road users is also going to change. Bearing all these aspects in mind, our overriding goal remains to make the traffic on our roads safer. Many of the activities in this context are summarized in the Mercedes-Benz “Vision of accident-free driving.” The current plateauing of r
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Schöneburg, Rodolfo, and Karl-Heinz Baumann. "What the Car Industry Can Do, Mercedes-Benz’ View." In The Vision Zero Handbook. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-23176-7_29-1.

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AbstractThe car industry faces some extreme challenges. Alongside the key task that we face of making individual transport climate-neutral, and alongside the new technological opportunities for communication and networking across our whole environment, it is clear that the user behavior of vehicle drivers and of all other road users is also going to change. Bearing all these aspects in mind, our overriding goal remains to make the traffic on our roads safer. Many of the activities in this context are summarized in the Mercedes-Benz “Vision of accident-free driving.” The current plateauing of r
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"Zero-Order Optimization Techniques." In Machine Learning Refined. Cambridge University Press, 2020. http://dx.doi.org/10.1017/9781108690935.005.

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Neittaanmäki, P., M. Rudnicki, and A. Savini. "Unconstrained optimization." In Inverse Problems and Optimal Design in Electricity and Magnetism. Oxford University PressOxford, 1996. http://dx.doi.org/10.1093/oso/9780198593836.003.0006.

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Abstract As can be seen, the higher-order derivatives of a quadratic function are all zero. From a Taylor expansion (the first three terms are always exact) it follows that all functions with continuous second derivatives behave much the same as quadratic functions provided the region is small enough. This fact is essential for many optimization algorithms which minimize a quadratic function in a finite number of steps.
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Calvano Flavio, Caturano Gennaro, Cavaccini Giovanni, Rubinacci Guglielmo, Tamburrino Antonello, and Ventre Salvatore. "Crack Shape Reconstruction from ECT Using Topology Constrained Optimization (TOPCSA) Algorithms." In Studies in Applied Electromagnetics and Mechanics. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-554-9-230.

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In this contribution the reconstruction of crack shapes from experimental data acquired on a damaged test plate at eddy current frequencies is performed by means of two different zero-order iterative optimization methods: the Genetic Algorithm (GA) and the Clonal Selection Algorithm for Topology Optimization (TOPCSA). A comparison of the performances of the algorithms on ECT experimental data shows the superior behavior of the TOPCSA with respect to the one of the GA.
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Conference papers on the topic "Zero-Order optimization"

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Bar, Noga, and Raja Giryes. "ZOQO: Zero-Order Quantized Optimization." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10887815.

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Deng, Yichuan, Zhihang Li, Sridhar Mahadevan, and Zhao Song. "Zero-th Order Algorithm for Softmax Attention Optimization." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825630.

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An, Qi, Jianxiao Wang, Shengyu Wu, Weikai Ren, Ruanming Huang, and Gengyin Li. "Zero-Order Optimization in Security Constrained Power System Scheduling." In 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2024. http://dx.doi.org/10.1109/icecet61485.2024.10698329.

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Cao, Jianfei, Yaobing Wang, and Jiyang Yu. "Lunar Rover's Path Tracking Control Strategy Using Zero-Order Optimization Method." In 2024 IEEE International Conference on Control Science and Systems Engineering (ICCSSE). IEEE, 2024. https://doi.org/10.1109/iccsse63803.2024.10823689.

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Baumgartner, Katrin, and Moritz Diehl. "Zero-Order Optimization-Based Iterative Learning Control." In 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020. http://dx.doi.org/10.1109/cdc42340.2020.9303854.

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Tang, Yujie, and Na Li. "Distributed Zero-Order Algorithms for Nonconvex Multi-Agent optimization." In 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2019. http://dx.doi.org/10.1109/allerton.2019.8919690.

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Mohr, Charles A., Jonathan W. Owen, Shannon D. Blunt, and Christopher T. Allen. "Zero-Order Reconstruction Optimization of Waveforms (ZOROW) for Modest DAC Rates." In 2020 IEEE International Radar Conference (RADAR). IEEE, 2020. http://dx.doi.org/10.1109/radar42522.2020.9114615.

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Lee, Madison, Shubhanshu Shekhar, and Tara Javidi. "Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS." In 2022 IEEE International Symposium on Information Theory (ISIT). IEEE, 2022. http://dx.doi.org/10.1109/isit50566.2022.9834683.

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Zhang, Dawei, Yuanshen Huang, Zhengji Ni, Jiabi Chen, Songlin Zhuang, and Hongbo He. "The optimization of zero-order diffractive filters for security imaging applications." In Sixth International Conference on Thin Film Physics and Applications. SPIE, 2008. http://dx.doi.org/10.1117/12.792249.

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Baumgärtner, Katrin, and Moritz Diehl. "Local Convergence Analysis of Damping for Zero-Order Optimization-Based Iterative Learning Control." In 2023 European Control Conference (ECC). IEEE, 2023. http://dx.doi.org/10.23919/ecc57647.2023.10178225.

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