Relevant bibliographies by topics / Classical optimization

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Classical optimization'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Classical optimization"

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Bilimoria, K. D., E. M. Cliff, and H. J. Kelley. "Classical and neo-classical cruise-dash optimization." Journal of Aircraft 22, no. 7 (1985): 555–60. http://dx.doi.org/10.2514/3.45165.

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Moiseenko, V. V., and V. V. Yatskevich. "System optimization as a generalization of classical optimization." Cybernetics and Systems Analysis 33, no. 3 (1997): 416–19. http://dx.doi.org/10.1007/bf02733075.

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Erschen, Stefan, Fabian Duddeck, and Markus Zimmermann. "Robust Design using classical optimization." PAMM 15, no. 1 (2015): 565–66. http://dx.doi.org/10.1002/pamm.201510272.

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Barkoutsos, Panagiotis Kl, Giacomo Nannicini, Anton Robert, Ivano Tavernelli, and Stefan Woerner. "Improving Variational Quantum Optimization using CVaR." Quantum 4 (April 20, 2020): 256. http://dx.doi.org/10.22331/q-2020-04-20-256.

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Hybrid quantum/classical variational algorithms can be implemented on noisy intermediate-scale quantum computers and can be used to find solutions for combinatorial optimization problems. Approaches discussed in the literature minimize the expectation of the problem Hamiltonian for a parameterized trial quantum state. The expectation is estimated as the sample mean of a set of measurement outcomes, while the parameters of the trial state are optimized classically. This procedure is fully justified for quantum mechanical observables such as molecular energies. In the case of classical optimizat
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van Apeldoorn, Joran, András Gilyén, Sander Gribling, and Ronald de Wolf. "Convex optimization using quantum oracles." Quantum 4 (January 13, 2020): 220. http://dx.doi.org/10.22331/q-2020-01-13-220.

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We study to what extent quantum algorithms can speed up solving convex optimization problems. Following the classical literature we assume access to a convex set via various oracles, and we examine the efficiency of reductions between the different oracles. In particular, we show how a separation oracle can be implemented using O~(1) quantum queries to a membership oracle, which is an exponential quantum speed-up over the Ω(n) membership queries that are needed classically. We show that a quantum computer can very efficiently compute an approximate subgradient of a convex Lipschitz function. C
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Yu, G. "Min-Max Optimization of Several Classical Discrete Optimization Problems." Journal of Optimization Theory and Applications 98, no. 1 (1998): 221–42. http://dx.doi.org/10.1023/a:1022601301102.

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Amy, Matthew, and Joseph Lunderville. "Linear and Non-linear Relational Analyses for Quantum Program Optimization." Proceedings of the ACM on Programming Languages 9, POPL (2025): 1072–103. https://doi.org/10.1145/3704873.

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The phase folding optimization is a circuit optimization used in many quantum compilers as a fast and effective way of reducing the number of high-cost gates in a quantum circuit. However, existing formulations of the optimization rely on an exact, linear algebraic representation of the circuit, restricting the optimization to being performed on straightline quantum circuits or basic blocks in a larger quantum program. We show that the phase folding optimization can be re-cast as an affine relation analysis , which allows the direct application of classical techniques for affine relations to e
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Suh, M. W., J. M. Yu, and Je Hyeng Lee. "Crack Identification Using Classical Optimization Technique." Key Engineering Materials 183-187 (April 2000): 61–66. http://dx.doi.org/10.4028/www.scientific.net/kem.183-187.61.

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Ellerman, David P. "AN ARBITRAGE INTERPRETATION OF CLASSICAL OPTIMIZATION." Metroeconomica 41, no. 3 (1990): 259–76. http://dx.doi.org/10.1111/j.1467-999x.1990.tb00469.x.

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Boţ, Radu Ioan, Sorin-Mihai Grad, and Gert Wanka. "Classical linear vector optimization duality revisited." Optimization Letters 6, no. 1 (2010): 199–210. http://dx.doi.org/10.1007/s11590-010-0263-1.

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Dissertations / Theses on the topic "Classical optimization"

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Elallam, Abderrahim. "Constructions & Optimization in Classical Real Analysis Theorems." Digital Commons @ East Tennessee State University, 2021. https://dc.etsu.edu/etd/3901.

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This thesis takes a closer look at three fundamental Classical Theorems in Real Analysis. First, for the Bolzano Weierstrass Theorem, we will be interested in constructing a convergent subsequence from a non-convergent bounded sequence. Such a subsequence is guaranteed to exist, but it is often not obvious what it is, e.g., if an = sin n. Next, the H¨older Inequality gives an upper bound, in terms of p ∈ [1,∞], for the the integral of the product of two functions. We will find the value of p that gives the best (smallest) upper-bound, focusing on the Beta and Gamma integrals. Finally, for the
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Rava, Andrea Basilio. "Quantum approximate optimization algorithm: combinatorial problems and classical statistical models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23113/.

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The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm for solving combinatorial optimization problems. Since most of combinatorial optimization problems may be thought as particular instances of Ising Hamiltonians, the study of the QAOA is very relevant from the physical point of view for its potential applications in describing physical systems. In the QAOA a quantum state is prepared and, through 2p parameterized quantum evolutions, a final state which represents an extreme of cost function and encodes the approximate solution of the problem is obtaine
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Vo, Thi Quynh Trang. "Algorithms and Machine Learning for fair and classical combinatorial optimization." Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2024. http://www.theses.fr/2024UCFA0035.

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L'optimisation combinatoire est un domaine des mathématiques dans lequel un problème consiste à trouver une solution optimale dans un ensemble fini d'objets. Elle a des applications cruciales dans de nombreux domaines. Le branch-and-cut est l'un des algorithmes les plus utilisés pour résoudre exactement des problèmes d'optimisation combinatoire. Dans cette thèse, nous nous concentrons sur les aspects informatiques du branch-and-cut et plus particulièrement, sur deux aspects importants de l'optimisation combinatoire: l'équité des solutions et l'intégration de l'apprentissage automatique. Dans l
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Semerjian, Guilhem. "Mean-field disordered systems : glasses and optimization problems, classical and quantum." Habilitation à diriger des recherches, Ecole Normale Supérieure de Paris - ENS Paris, 2013. http://tel.archives-ouvertes.fr/tel-00785924.

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Ce mémoire présente mes activités de recherche dans le domaine de la mécanique statistique des systèmes désordonnés, en particulier sur les modèles de champ moyen à connectivité finie. Ces modèles présentent de nombreuses transitions de phase dans la limite thermodynamique, avec des applications tant pour la physique des verres que pour leurs liens avec des problèmes d'optimisation de l'informatique théorique. Leur comportement sous l'effet de fluctuations quantiques est aussi discuté, en lien avec des perspectives de calcul quantique.
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Hirani, Shyam, and Jonas Wallström. "The Black-Litterman Asset Allocation Model : An Empirical Comparison to the Classical Mean-Variance Framework." Thesis, Linköpings universitet, Nationalekonomi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-111570.

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Within the scope of this thesis, the Black-Litterman Asset Allocation Model (as presented in He & Litterman, 1999) is compared to the classical mean-variance framework by simulating past performance of portfolios constructed by both models using identical input data. A quantitative investment strategy which favours stocks with high dividend yield rates is used to generate private views about the expected excess returns for a fraction of the stocks included in the sample. By comparing the ex-post risk-return characteristics of the portfolios and performing ample sensitivity analysis with re
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李寶榮 and Po-wing Lee. "Integrated modern-heuristic and B/B approach for the classical traveling salesman problem on a parallel computer." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31222997.

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Lee, Po-wing. "Integrated modern-heuristic and B/B approach for the classical traveling salesman problem on a parallel computer /." Hong Kong : University of Hong Kong, 1999. http://sunzi.lib.hku.hk/hkuto/record.jsp?B21904315.

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Claewplodtook, Pana. "Optimization of nonlinear dynamic systems without Lagrange multipliers." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178654973.

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Lei, Weidong. "Cyclic Hoist Scheduling Problems in Classical and Sustainabl." Thesis, Belfort-Montbéliard, 2014. http://www.theses.fr/2014BELF0244/document.

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Les ateliers de traitement de surface automatisés, qui utilisent des robots de manutention commandés par ordinateur pour le transport de la pièce, ont été largement mis en place dans différents types d'entreprises industrielles, en raison de ses nombreux avantages par rapport à un mode de production manuel, tels que : une plus grande productivité, une meilleure qualité des produits, et l’impact sur les rythmes de travail. Notre recherche porte sur trois types de problèmes d'ordonnancement associés à ces systèmes, appelés Hoist Scheduling Problems, caractérisés par des contraintes de fenêtres d
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del, Valle Yamille E. "Optimization of power system performance using facts devices." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29612.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.<br>Committee Chair: Dr. Ronald G. Harley; Committee Member: Dr. Bonnie Heck; Committee Member: Dr. Deepak Divan; Committee Member: Dr. Ganesh K. Venayagamoorthy; Committee Member: Dr. Miroslav Begovic. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Books on the topic "Classical optimization"

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Razumikhin, B. S. Classical Principles and Optimization Problems. Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3995-0.

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Razumikhin, Boris Sergeevich. Classical principles and optimization problems. D. Reidel Pub. Co., 1987.

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Yukich, Joseph E. Probability Theory of Classical Euclidean Optimization Problems. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0093472.

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Yukich, Joseph. Probability theory of classical Euclidean optimization problems. Springer, 1998.

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service), SpringerLink (Online, ed. Calculus of Variations, Classical and Modern. Springer-Verlag Berlin Heidelberg, 2011.

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Helton, J. William. Classical control using H [infinity] methods: Theory, optimization, and design. SIAM, 1998.

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Vasiljević, Darko. Classical and Evolutionary Algorithms in the Optimization of Optical Systems. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1051-2.

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Vasiljević, Darko. Classical and evolutionary algorithms in the optimization of optical systems. Kluwer Academic Publishers, 2002.

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Vasiljević, Darko. Classical and Evolutionary Algorithms in the Optimization of Optical Systems. Springer US, 2002.

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Nelles, Oliver. Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Springer Berlin Heidelberg, 2001.

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Book chapters on the topic "Classical optimization"

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Berck, Peter, and Knut Sydsæter. "Classical optimization." In Economists’ Mathematical Manual. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-02678-6_13.

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Berck, Peter, and Knut Sydsæter. "Classical optimization." In Economists’ Mathematical Manual. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-11597-8_13.

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Gass, Saul I., and Carl M. Harris. "Classical optimization." In Encyclopedia of Operations Research and Management Science. Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_117.

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Sydsæter, Knut, Arne Strøm, and Peter Berck. "Classical optimization." In Economists’ Mathematical Manual. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03995-3_14.

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Khan, Aman. "Classical Optimization." In Cost and Optimization in Government. Routledge, 2017. http://dx.doi.org/10.4324/9781315207674-6.

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Sydsæter, Knut, Arne Strøm, and Peter Berck. "Classical optimization." In Economists’ Mathematical Manual. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-28518-2_14.

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Khan, Aman. "Classical Optimization." In Cost and Optimization in Government, 3rd ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003275183-10.

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Zlobec, Sanjo. "Classical Optimality Conditions." In Applied Optimization. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-0011-7_2.

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Jin, Yaochu, Handing Wang, and Chaoli Sun. "Classical Optimization Algorithms." In Studies in Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74640-7_2.

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Ansari, Qamrul Hasan, Elisabeth Köbis, and Jen-Chih Yao. "Classical Methods in Vector Optimization." In Vector Optimization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63049-6_4.

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Conference papers on the topic "Classical optimization"

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Udrescu, Mihai. "Turning the Tables in Hybrid Classical-Quantum Systems—Classical Optimization of Quantum Algorithms: Plenary Talk." In 2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2024. http://dx.doi.org/10.1109/saci60582.2024.10619882.

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Karthiga, M., D. Deepa, and C. Suganthi Evangeline. "Hybrid Quantum-Classical Phase optimization in Reflecting PMS beamforming." In 2025 Fourth International Conference on Smart Technologies, Communication and Robotics (STCR). IEEE, 2025. https://doi.org/10.1109/stcr62650.2025.11019579.

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Awasthi, Abhishek, Nico Kraus, Florian Krellner, and David Zambrano. "Real World Application of Quantum-Classical Optimization for Production Scheduling." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.10285.

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Bucher, David, Daniel Porawski, Benedikt Wimmer, et al. "Grid Cost Allocation in Peer-to-Peer Electricity Markets: Benchmarking Classical and Quantum Optimization Approaches." In Workshop on Quantum Artificial Intelligence and Optimization 2025. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013391400003890.

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Nour Sadoun, Maria Sara, Evangelos Piliouras, and Taous-Meriem Laleg-Kirati. "Backward Gradient Interval Optimization Convergence for the Semi-Classical Signal Analysis." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715029.

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R, Adithya, Joyline G. Dsa, Adarsh Rag S, and Suhas A. Bhyratae. "Hybrid Quantum-Classical Neural Networks and its Hyperparameter Optimization-A Study." In 2024 International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC). IEEE, 2024. https://doi.org/10.1109/cosmic63293.2024.10871871.

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Emami, Babak, David Haycraft, Carrie Spear, Lac Nguyen, Mohammad-Ali Miri, and Nicholas Chancellor. "Financial fraud detection with entropy quantum optimization versus classical machine learning." In Quantum Information Science, Sensing, and Computation XVII, edited by Michael L. Fanto and Michael Hayduk. SPIE, 2025. https://doi.org/10.1117/12.3066304.

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Nguyen, Doan Hieu, Xuan Tung Nguyen, and Won Joo Hwang. "Pilot Allocation Optimization by Hybrid Quantum-Classical Neural Network in CF-mMIMO." In 2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2025. https://doi.org/10.1109/icaiic64266.2025.10920772.

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Lim, Qi Jian, and Zhen Peng. "Hybrid Quantum-Classical Algorithms for Satellite Swarm Optimization in Non-Terrestrial Networks." In 2025 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM). IEEE, 2025. https://doi.org/10.23919/usnc-ursinrsm66067.2025.10906859.

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Qu, Kaiping, Yue Chen, Changhong Zhao, Shihan Huang, and Yan Xu. "A quantum-classical hybrid optimization method for restoration of active distribution networks." In 2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2024. https://doi.org/10.1109/ei264398.2024.10991588.

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Reports on the topic "Classical optimization"

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Chindelevitch, Leonid. Decision Optimization with Classical and Modern AI. Instats Inc., 2025. https://doi.org/10.61700/1xg8xsq3izwbt1502.

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This three-day workshop equips PhD students, academics, and data scientists with comprehensive insights into decision optimization, emphasizing applications across a broad range of fields. Participants will gain theoretical knowledge and practical skills using industry-standard software such as GLPK, and platforms including CPLEX and Gurobi, preparing them to tackle complex optimization challenges in research and practice.
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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Radvand, Tina, and Alireza Talebpour. A Quantum Optimization Algorithm for Optimal Electric Vehicle Charging Station Placement for Intercity Trips. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-028.

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Electric vehicles (EVs) play a significant role in enhancing the sustainability of transportation systems. However, their widespread adoption is hindered by inadequate public charging infrastructure, particularly to support long-distance travel. Identifying optimal charging station locations in large transportation networks presents a well-known NP-hard combinatorial optimization problem, as the search space grows exponentially with the number of potential charging station locations. This report introduces a quantum search-based optimization algorithm designed to enhance the efficiency of solv
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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