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Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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1

Aull, Mark J. "Airborne Wind Energy System Analysis and Design Optimization." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592168644639446.

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Kůdela, Jakub. "Advanced Decomposition Methods in Stochastic Convex Optimization." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-403864.

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Při práci s úlohami stochastického programování se často setkáváme s optimalizačními problémy, které jsou příliš rozsáhlé na to, aby byly zpracovány pomocí rutinních metod matematického programování. Nicméně, v některých případech mají tyto problémy vhodnou strukturu, umožňující použití specializovaných dekompozičních metod, které lze použít při řešení rozsáhlých optimalizačních problémů. Tato práce se zabývá dvěma třídami úloh stochastického programování, které mají speciální strukturu, a to dvoustupňovými stochastickými úlohami a úlohami s pravděpodobnostním omezením, a pokročilými dekompozi
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3

Cheng, Jianqiang. "Stochastic Combinatorial Optimization." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA112261.

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Dans cette thèse, nous étudions trois types de problèmes stochastiques : les problèmes avec contraintes probabilistes, les problèmes distributionnellement robustes et les problèmes avec recours. Les difficultés des problèmes stochastiques sont essentiellement liées aux problèmes de convexité du domaine des solutions, et du calcul de l’espérance mathématique ou des probabilités qui nécessitent le calcul complexe d’intégrales multiples. A cause de ces difficultés majeures, nous avons résolu les problèmes étudiées à l’aide d’approximations efficaces.Nous avons étudié deux types de problèmes stoch
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4

Fei, Lin. "On a stochastic optimization technique : stochastic probing /." The Ohio State University, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487777901661535.

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5

Birbil, Sevket Ilker. "Stochastic Global Optimization Techniques." NCSU, 2002. http://www.lib.ncsu.edu/theses/available/etd-20020403-171452.

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<p>In this research, a novel population-based global optimization method has been studied. The method is called Electromagnetism-like Mechanism or in short EM. The proposed method mimicks the behavior of electrically charged particles. In other words, a set of points is sampled from the feasible region and these points imitate the role of the charged particles in basic electromagnetism. The underlying idea of the method is directing sample points toward local optimizers, which point out attractive regions of the feasible space.The proposed method has been applied to different test problems fro
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6

Parpas, Panayiotis. "Algorithms for stochastic optimization." Thesis, Imperial College London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434980.

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7

Pohl, Jan. "Algoritmus s pravděpodobnostním směrovým vektorem." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-233694.

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This disertation presents optimization algorithm with probability direction vector. This algorithm, in its basic form, belongs to category of stochastic optimization algorithms. It uses statistically effected perturbation of individual through state space. This work also represents modification of basic idea to the form of swarm optimization algoritm. This approach contains form of stochastic cooperation. This is one of the new ideas of this algorithm. Population of individuals cooperates only through modification of probability direction vector and not directly. Statistical tests are used to
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8

Xiong, Xiaoping. "Stochastic optimization algorithms and convergence /." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2360.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.<br>Thesis research directed by: Business and Management. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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9

Al-Mharmah, Hisham. "Global optimization of stochastic functions." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/25665.

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10

李國誠 and Kwok-shing Lee. "Convergences of stochastic optimization algorithms." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B3025632X.

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11

Youssef, Nataly. "Stochastic analysis via robust optimization." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/103246.

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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2016.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 167-174).<br>To evaluate the performance and optimize systems under uncertainty, two main avenues have been suggested in the literature: stochastic analysis and optimization describing the uncertainty probabilistically and robust optimization describing the uncertainty deterministically. Instead, we propose a novel paradigm which leverages the conclusions of probability theor
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12

Theodosopoulos, Theodore. "Stochastic models for global optimization." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11404.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.<br>Includes bibliographical references (p. 61-63).<br>by Theodore Vassilios Theodosopoulos.<br>Ph.D.
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13

Liu, Liu. "Stochastic Optimization in Machine Learning." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/19982.

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Stochastic optimization has received extensive attention in recent years due to their extremely potential for solving the large-scale optimization problem. However, the classical optimization algorithm and original stochastic method might prove to be inefficient due to the fact that: 1) the cost-per-iteration is a computational challenge, 2) the convergence and complexity are poorly performed. In this thesis, we exploit the stochastic optimization from three kinds of "order" optimization to address the problem. For the stochastic zero-order optimization, we introduce a novel variance reduction
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14

Dagreou, Mathieu. "Contributions to stochastic bilevel optimization." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG041.

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Les problèmes bi-niveaux sont un type de problème d'optimisation caractérisés par une structure hiérarchique. Dans ces problèmes, on cherche à minimiser une fonction externe sous la contrainte que certaines variables minimisent une fonction interne. Ces problèmes gagnent en popularité dans la communauté du machine learning en raison de leur large éventail d'applications, telles que l'optimisation des hyperparamètres.Dans cette thèse, nous explorons des algorithmes basés sur la différenciation implicite approximée pour aborder les problèmes bi-niveaux où les fonctions externes et internes sont
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15

Červenka, Daniel. "Aplikace metod optimalizace zásob v dodavatelských řetězcích." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-150222.

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As in the stock of trading business is allocated a large part of the capital resources, it is necessary to determine the manner of their control. For this purpose a number of models were developed. Before application to the specific case, these models must be properly adjusted to ensure conformity with reality. The aim of this thesis is to optimize the inventory management of electronic commerce. The stochastic model with loss from unfulfilled orders was chosen as default. First, the necessary adjustments were made to the model and defined input parameters. After filling model with real data,
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16

Homem, de Mello Tito. "Simulation-based methods for stochastic optimization." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/24846.

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17

Carvalho, Miguel de. "Extremum estimators and stochastic optimization methods." Doctoral thesis, Faculdade de Ciências e Tecnologia, 2009. http://hdl.handle.net/10362/5899.

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Submitted in partial fulfillment for the Requirements for the Degree of PhD in Mathematics, in the Speciality of Statistics in the Faculdade de Ciências e Tecnologia<br>Extremum estimators is one of the broadest class of statistical methods for the obtention of consistent estimates. The Ordinary Least Squares (OLS), the Generalized Method of Moments (GMM) as well as the Maximum Likelihood (ML) methods are all given as solutions to an optimization problem of interest, and thus are particular instances of extremum estimators. One major concern regarding the computation of estimates of this type
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18

Kosuch, Stefanie. "Stochastic Optimization Problems with Knapsack Constraint." Paris 11, 2010. http://www.theses.fr/2010PA112154.

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Etant donné un ensemble d'objets, chacun ayant un poids et une valeur. Le problème de sac-à-dos consiste à choisir un sous-ensemble d'objets qui (i) respecte une certaine restriction du poids (la capacité du sac-à-dos) et (ii) dont la valeur totale est maximisée. Dans cette thèse nous étudions quatre problèmes d'optimisation stochastique avec contrainte de sac-à-dos: le problème de sac-à-dos avec recours simple, le problème de sac-à-dos avec contrainte probabiliste, le problème de sac-à-dos avec recours et un problème bi-niveau stochastique avec contrainte de sac-à-dos probabiliste. Les problè
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19

Wei, Xiaofan. "Stochastic Analysis and Optimization of Structures." University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1163789451.

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20

Null, Brad. "Stochastic modeling and optimization in baseball /." May be available electronically:, 2009. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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21

Park, Chuljin. "Discrete optimization via simulation with stochastic constraints." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49088.

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In this thesis, we first develop a new method called penalty function with memory (PFM). PFM consists of a penalty parameter and a measure of constraint violation and it converts a discrete optimization via simulation (DOvS) problem with stochastic constraints into a series of DOvS problems without stochastic constraints. PFM determines a penalty of a visited solution based on past results of feasibility checks on the solution. Specifically, assuming a minimization problem, a penalty parameter of PFM, namely the penalty sequence, diverges to infinity for an infeasible solution but converges to
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22

Bouakiz, Mokrane. "Risk-sensitivity in stochastic optimization with applications." Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/25457.

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23

Krishnaswamy, Ravishankar. "Approximation Techniques for Stochastic Combinatorial Optimization Problems." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/157.

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The focus of this thesis is on the design and analysis of algorithms for basic problems in Stochastic Optimization, specifically a class of fundamental combinatorial optimization problems where there is some form of uncertainty in the input. Since many interesting optimization problems are computationally intractable (NP-Hard), we resort to designing approximation algorithms which provably output good solutions. However, a common assumption in traditional algorithms is that the exact input is known in advance. What if this is not the case? What if there is uncertainty in the input? With the gr
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24

Vondrák, Jan 1974. "Probabilistic methods in combinatorial and stochastic optimization." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/28827.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2005.<br>Includes bibliographical references (leaves 103-106).<br>(cont.) Packing/Covering problems, we prove upper and lower bounds on the adaptivity gap depending on the dimension. We also design polynomial-time algorithms achieving near-optimal approximation guarantees with respect to the adaptive optimum. Finally, we prove complexity-theoretic results regarding optimal adaptive policies. These results are based on a connection between adaptive policies and Arthur-Merlin games which yields PSPACE-hardness results
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25

Jauffred, M. Francisco Javier. "Stochastic optimization for robust planning in transportation." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10028.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1998.<br>Includes bibliographical references (leaves 116-118).<br>by Francisco J. Jauffred.<br>Ph.D.
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26

Pedraz, Ana Mana. "Stochastic optimization of aero-engine maintenance schedules." Thesis, Kingston University, 2010. http://eprints.kingston.ac.uk/20875/.

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In this thesis the maintenance scheduling optimization problem of a multi-component system is formulated as a multi-objective stochastic dynamic problem, which is solved by first transforming the multi-objective formulation into a single-objective one via the weighted-sum method, and then using an iterated two-stage stochastic linear programming approach to solve the stochastic problem. The main application is the maintenance of a commercial gas turbine aero-engine. The model determines optimal opportunistic replacement schedules considering both deterministic and stochastic engine configurati
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27

Maglaras, George K. "Integrated analysis and design in stochastic optimization." Thesis, Virginia Tech, 1989. http://hdl.handle.net/10919/46048.

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<p>When structural optimization is performed via an iterative solution technique, it is possible to integrate the analysis and design iterations, in an integrated analysis and design procedure. The present work seeks to apply an integrated analysis and design approach in reliability based optimization, when a safety index approach is used.</p> <p>Two variants of the new approach are presented. Both of them are based on partially converged solution of the optimization procedure. The safety index approach employed allows us to use semi-analytical formulas to calculate the sensitivity der
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28

Wilhelm, Paul Allan. "Pheromone particle swarm optimization of stochastic systems." [Ames, Iowa : Iowa State University], 2008.

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29

Leclere, Vincent. "Contributions to decomposition methods in stochastic optimization." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1092/document.

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Le contrôle optimal stochastique (en temps discret) s'intéresse aux problèmes de décisions séquentielles sous incertitude. Les applications conduisent à des problèmes d'optimisation degrande taille. En réduisant leur taille, les méthodes de décomposition permettent le calcul numérique des solutions. Nous distinguons ici deux formes de décomposition. La emph{décomposition chaînée}, comme la Programmation Dynamique, résout successivement, des sous-problèmes de petite taille. La décomposition parallèle, comme le Progressive Hedging, consiste à résoudre itérativement et parallèlement les sous-prob
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Barra, Olivares Erick Andrés de la. "Stochastic optimization applied to scheduling mining problems." Tesis, Universidad de Chile, 2019. http://repositorio.uchile.cl/handle/2250/171801.

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Mrázková, Eva. "Approximations in Stochastic Optimization and Their Applications." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2010. http://www.nusl.cz/ntk/nusl-233932.

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Mnoho inženýrských úloh vede na optimalizační modely s~omezeními ve tvaru obyčejných (ODR) nebo parciálních (PDR) diferenciálních rovnic, přičemž jsou v praxi často některé parametry neurčité. V práci jsou uvažovány tři inženýrské problémy týkající se optimalizace vibrací a optimálního návrhu rozměrů nosníku. Neurčitost je v nich zahrnuta ve formě náhodného zatížení nebo náhodného Youngova modulu. Je zde ukázáno, že dvoustupňové stochastické programování nabízí slibný přístup k řešení úloh daného typu. Odpovídající matematické modely, zahrnující ODR nebo PDR omezení, neurčité parametry a více
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32

Dokov, Steftcho Pentchev. "Deterministic approximations in stochastic programming with applications to a class of portfolio allocation problems." Access restricted to users with UT Austin EID Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3031041.

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33

Hashemi, Fatemeh Sadat. "Sampling Controlled Stochastic Recursions: Applications to Simulation Optimization and Stochastic Root Finding." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/76740.

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We consider unconstrained Simulation Optimization (SO) problems, that is, optimization problems where the underlying objective function is unknown but can be estimated at any chosen point by repeatedly executing a Monte Carlo (stochastic) simulation. SO, introduced more than six decades ago through the seminal work of Robbins and Monro (and later by Kiefer and Wolfowitz), has recently generated much attention. Such interest is primarily because of SOs flexibility, allowing the implicit specification of functions within the optimization problem, thereby providing the ability to embed virtually
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Nagaraj, Kalyani Shankar. "Stochastically Constrained Simulation Optimization On Mixed-Integer Spaces." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/50614.

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We consider the problem of identifying solutions to a stochastic system under multiple constraints. The objective function and the constraints are expressed in terms of performance measures of the system that are observable only via a simulation model parameterized by a finite number of decision variables. In solving for such a system, one faces the much harder challenge of verifying the feasibility of a potential solution. Toward this, we present cgR-SPLINE, a multistart simulation optimization (SO) algorithm on integer spaces. cgR-SPLINE sequentially solves random restarts of a gradient-bas
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35

Ardic, Sureyya. "Funding site cleanup at closing Army installations : a stochastic optimization approach." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA401415.

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Thesis (M.S. in Operations Research) Naval Postgraduate School, December 2001.<br>Thesis Advisor (s): Dell, Robert F. "December 2001." Includes bibliographical references (p. 35). Also available online.
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36

Blomvall, Jörgen. "Optimization of financial decisions using a new stochastic programming method /." Linköping : Univ, 2001. http://www.bibl.liu.se/liupubl/disp/disp2001/tek676s.pdf.

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37

Vardanyan, Yelena. "On stochastic optimization for short-term hydropower planning." Licentiate thesis, KTH, Elektriska energisystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-104638.

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Renewable generation is the fastest growing energy resources in the past decade. Renewable energy sources, particularly wind power, provide clean and environmentally friendly energy to meet the system demand, meanwhile introducing huge levels of uncertainty in the system. On the one hand the deregulated electric power industry and on the other hand the intermittent nature of renewable energy sources cause highly volatile and uncertain electricity prices in different market places. This will create challenges for economical operation and planning of the flexible energy sources, particularly hyd
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38

Parruca, Donald [Verfasser]. "Stochastic Optimization in OFDMA/LTE Networks / Donald Parruca." Aachen : Shaker, 2016. http://d-nb.info/1101184213/34.

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Anagnostopoulou, Vasiliki. "Sturmian Measures and Stochastic Dominance in Ergodic Optimization." Thesis, Queen Mary, University of London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522324.

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40

Ali, M. "Some modified stochastic global optimization algorithms with applications." Thesis, Loughborough University, 1994. https://dspace.lboro.ac.uk/2134/13429.

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Stochastic methods for global optimization problems with continuous variables have been studied. Modifications of three different algorithms have been proposed. These are (1) Multilevel Single Linkage (MSL), (2) Simulated Annealing (SA) and (3) Controlled Random Search (CRS). We propose a new topographical Multilevel Single Linkage (TMSL) algorithm as an extension of MSL. TMSL performs much better than MSL, especially in terms of number of function evaluations. A new aspiration based simulated annealing algorithm (ASA) has been derived which enhances the performance of SA by incorporating an a
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41

Zhan, Minshu. "Program auto-tuning through population-based stochastic optimization." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106451.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 41-42).<br>Program optimization often can and need to be decoupled from the programs innate logic. In various domains, program autotuners have been developed which can automatically search for optimal program configurations using established optimization methods. OpenTuner is a general-purpose autotuner which provides a flexible search domain representation and robust optimization techniqu
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42

Brownfield, Daniel Bradley. "Geometrical optimization of arched structures under stochastic loading." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104189.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 53).<br>This thesis presents a method for determining the moment-optimized shape y(x) for arched structures under unpredictable loading scenarios. A frame geometry optimization derivation is presented that demonstrates the relationship between certain unpredicted loads and an equivalent guaranteed loading condition that is more easily solvable through standard equilibrium analysis. The relationshi
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43

Angulo, Olivares Gustavo I. "Integer programming approaches for semicontinuous and stochastic optimization." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51862.

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This thesis concerns the application of mixed-integer programming techniques to solve special classes of network flow problems and stochastic integer programs. We draw tools from complexity and polyhedral theory to analyze these problems and propose improved solution methods. In the first part, we consider semi-continuous network flow problems, that is, a class of network flow problems where some of the variables are required to take values above a prespecified minimum threshold whenever they are not zero. These problems find applications in management and supply chain models where orders in s
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Konstantelos, Ioannis. "A stochastic optimization framework for anticipatory transmission investment." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/14649.

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An unprecedented amount of renewable generation is to be connected to the UK grid in the coming decades, giving rise to new power flow patterns and warranting unprecedented amounts of transmission investment. However, significant uncertainty surrounds the state of the electricity system, primarily in terms of the size, location and type of new generators to be connected. These sources of uncertainty render the system planner unable to make fully informed decisions about future transmission investment. This thesis presents a stochastic formulation for the transmission expansion planning problem
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Konečný, Jakub. "Stochastic, distributed and federated optimization for machine learning." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/31478.

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We study optimization algorithms for the finite sum problems frequently arising in machine learning applications. First, we propose novel variants of stochastic gradient descent with a variance reduction property that enables linear convergence for strongly convex objectives. Second, we study distributed setting, in which the data describing the optimization problem does not fit into a single computing node. In this case, traditional methods are inefficient, as the communication costs inherent in distributed optimization become the bottleneck. We propose a communication-efficient framework whi
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46

Wei, Wei. "Stochastic Dynamic Optimization and Games in Operations Management." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1354751981.

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Yuan, Yang. "Algorithmic Advances in Stochastic Combinatorial Optimization and Applications." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1275433024.

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Fontaine, Xavier. "Sequential learning and stochastic optimization of convex functions." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM024.

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Dans cette thèse nous étudions plusieurs problèmes d'apprentissage automatique qui sont tous liés à la minimisation d'une fonction bruitée, qui sera souvent convexe.Du fait de leurs nombreuses applications nous nous concentrons sur des problèmes d'apprentissage séquentiel, qui consistent à traiter des données ``à la volée'', ou en ligne.La première partie de cette thèse est ainsi consacrée à l'étude de trois différents problèmes d'apprentissage séquentiel dans lesquels nous rencontrons le compromis classique ``exploration vs. exploitation''.Dans chacun de ces problèmes un agent doit prendre de
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Cen, Zhihao. "LNG portfolio optimization approach by stochastic programming technique." École polytechnique, 2010. http://www.theses.fr/2011EPXX0114.

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The work presented in this Ph. D dissertation is motivated by the problem of management of a team of cargos transporting liquid natural gas (LNG) initially proposed by Total. The holder of the portfolio has to meet its commitments towards its counterparts, while trying to generate ports through arbitrating different commodities market. Hence, the management of portfolio can be modeled as a stochastic, dynamic and integer optimization problem. The work consists mainly 3 parts: dual stochastic programming, sensitivity analysis, and heuristic exploration of integer stochastic programming<br>Le tr
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Wang, Chen. "Variants of Deterministic and Stochastic Nonlinear Optimization Problems." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112294/document.

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Les problèmes d’optimisation combinatoire sont généralement réputés NP-difficiles, donc il n’y a pas d’algorithmes efficaces pour les résoudre. Afin de trouver des solutions optimales locales ou réalisables, on utilise souvent des heuristiques ou des algorithmes approchés. Les dernières décennies ont vu naitre des méthodes approchées connues sous le nom de métaheuristiques, et qui permettent de trouver une solution approchées. Cette thèse propose de résoudre des problèmes d’optimisation déterministe et stochastique à l’aide de métaheuristiques. Nous avons particulièrement étudié la méthode de
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