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Статті в журналах з теми "Lagrange optimisation"
Xu, Yihong, and Chuanxi Zhu. "On super efficiency in set-valued optimisation in locally convex spaces." Bulletin of the Australian Mathematical Society 71, no. 2 (April 2005): 183–92. http://dx.doi.org/10.1017/s0004972700038168.
Повний текст джерелаVaidogas, E. R. "ON RELIABILITY-BASED STRUCTURAL OPTIMISATION USING STOCHASTIC QUASIGRADIENT METHODS/ZUR ZUVERLÄSSIGKEITSTHEORETISCH GESTÜTZTEN TRAGWERKS-OPTIMIERUNG MIT VERFAHREN DER STOCHASTISCHEN QUASIGRA-DIENTEN." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 1, no. 2 (June 30, 1995): 43–64. http://dx.doi.org/10.3846/13921525.1995.10531512.
Повний текст джерелаLi, Taiyong, and Yihong Xu. "The Strictly Efficient Subgradient of Set-Valued Optimisation." Bulletin of the Australian Mathematical Society 75, no. 3 (June 2007): 361–71. http://dx.doi.org/10.1017/s0004972700039290.
Повний текст джерелаAntczak, Tadeusz. "Saddle point criteria and duality in multiobjective programming via an η-approximation method". ANZIAM Journal 47, № 2 (жовтень 2005): 155–72. http://dx.doi.org/10.1017/s1446181100009962.
Повний текст джерелаRingis, Daniel J., François Pitié, and Anil Kokaram. "Per Clip Lagrangian Multiplier Optimisation for HEVC." Electronic Imaging 2020, no. 10 (January 26, 2020): 136–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.10.ipas-136.
Повний текст джерелаGóral, Ida, and Krzysztof Tchoń. "Lagrangian Jacobian Motion Planning: A Parametric Approach." Journal of Intelligent & Robotic Systems 85, no. 3-4 (July 13, 2016): 511–22. http://dx.doi.org/10.1007/s10846-016-0394-4.
Повний текст джерелаTizzi, S. "Polynomial series expansion for optimisation of wing plane structures in idealised critical flutter conditions." Aeronautical Journal 109, no. 1091 (January 2005): 23–33. http://dx.doi.org/10.1017/s0001924000000506.
Повний текст джерелаSommerfeld, Martin, and Silvio Schmalfuß. "Analysis and optimisation of particle mixing performance in fluid phase resonance mixers based on Euler/Lagrange calculations." Advanced Powder Technology 31, no. 1 (January 2020): 139–57. http://dx.doi.org/10.1016/j.apt.2019.10.006.
Повний текст джерелаWarner, Paul. "Use of Lagrange Multipliers to Provide an Approximate Method for the Optimisation of a Shield Radius and Contents." EPJ Web of Conferences 153 (2017): 06012. http://dx.doi.org/10.1051/epjconf/201715306012.
Повний текст джерелаKrzyżaniak, Stanisław. "Optimisation of the stock structure of a single stock item taking into account stock quantity constraints, using a lagrange multiplier." Logforum 18, no. 2 (June 30, 2022): 261–69. http://dx.doi.org/10.17270/j.log.2022.730.
Повний текст джерелаДисертації з теми "Lagrange optimisation"
Giri, Jason University of Ballarat. "Non-linear analogues of Lagrange functions in constrained optimization." University of Ballarat, 2005. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/12782.
Повний текст джерелаDoctor of Philosophy
Giri, Jason. "Non-linear analogues of Lagrange functions in constrained optimization." University of Ballarat, 2005. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/14618.
Повний текст джерелаDoctor of Philosophy
Monokrousos, Antonios. "Optimisation and control of boundary layer flows." Licentiate thesis, Stockholm : Skolan för teknikvetenskap, Kungliga Tekniska högskolan, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-10652.
Повний текст джерелаHemazro, Tekogan Dzigbodi. "Le problème de répartition des clientèles scolaires." Sherbrooke : Université de Sherbrooke, 1998.
Знайти повний текст джерелаSaissi, Fatima Ezzarha. "Optimisation à deux niveaux : Résultats d'existence, dualité et conditions d'optimalité." Thesis, Limoges, 2017. http://www.theses.fr/2017LIMO0030.
Повний текст джерелаSince its introduction, the class of tao-level programming problems has attracted increasing interest. Indeed, because of its applications in a multitude of concrete problems (management problems, economic planning, chemistry, environmental sciences,...), several researchers have been interested in the study of such class of problems. This thesis deals with the study of some classes of two-level optimization problems, namely, strong two-level problems, strong-weak two-level problems and semi-vectorial two-level problems. In the first chapter, we have recalled some definitions and results related to topology and convex analysis that we have used in our study. In the second chapter, we have discussed some theoretical and algorithmic results established in the literature for solving some classes of two-level optimization problems. The third chapter deals with strong-weak Stackelberg problems. As it is well-known, such a class of problems presents difficulties in its study concerning the existence of solutions. So that, for a strong-weak two-level optimization problem, we have first given a regularization. Then, via this regularization and under appropriate assumptions we have shown the existence of solutions to such a problem. This result generalizes the one given in the literature for weak Stackelberg problems. In the fourth chapter, we have given a duality approach for a strong two-level programming problem (S). The duality approach is based on the use of a regularization and the Fenchel-Lagrange duality. Then, via this approach, we have given necessary optimality conditions for (S). Finally, sufficient optimality conditions are given for the initial problem (S). An application to a two-level resource allocation problem is given. In the fifth chapter, we have considered a semivectorial two-level programming problem (SVBL) where the upper and lower levels are vectorial and scalar respectively. For such a problem, we have given a duality approach based on the use of a regularization, a scalarization and the Fenchel-Lagrange duality. Then, via this approach we have established necessary optimality conditions for (SVBL). Finally, we have given sufficient optimality conditions without using the duality approach
Monokrousos, Antonios. "Optimisation and control of shear flows." Doctoral thesis, KTH, Stabilitet, Transition, Kontroll, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-33771.
Повний текст джерелаQC 20110518
Grigoleit, Mark Ted. "Optimisation of large scale network problems." Thesis, Curtin University, 2008. http://hdl.handle.net/20.500.11937/1405.
Повний текст джерелаGrigoleit, Mark Ted. "Optimisation of large scale network problems." Curtin University of Technology, Department of Mathematics and Statistics, 2008. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=115092.
Повний текст джерелаWe then use this information to constrain the network along a bisecting meridian. The combination of Lagrange Relaxation (LR) and a heuristic for filtering along the meridian provide an aggressive method for finding near-optimal solutions in a short time. Two network problems are studied in this work. The first is a Submarine Transit Path problem in which the transit field contains four sonar detectors at known locations, each with the same detection profile. The side constraint is the total transit time, with the submarine capable of 2 speeds. For the single-speed case, the initial LR duality gap may be as high as 30%. The first hybrid method uses a single centre meridian to constrain the network based on the unused time resource, and is able to produce solutions that are generally within 1% of optimal and always below 3%. Using the computation time for the initial Lagrange Relaxation as a baseline, the average computation time for the first hybrid method is about 30% to 50% higher, and the worst case CPU times are 2 to 4 times higher. The second problem is a random valued network from the literature. Edge costs, times, and lengths are uniform, randomly generated integers in a given range. Since the values given in the literature problems do not yield problems with a high duality gap, the values are varied and from a population of approximately 100,000 problems only the worst 200 from each set are chosen for study. These problems have an initial LR duality gap as high as 40%. A second hybrid method is developed, using values for the unused time resource and the lower bound values computed by Dijkstra’s algorithm as part of the LR method. The computed values are then used to position multiple constraining meridians in order to allow LR to find better solutions.
This second hybrid method is able to produce solutions that are generally within 0.1% of optimal, with computation times that are on average 2 times the initial Lagrange Relaxation time, and in the worst case only about 5 times higher. The best method for solving the Constrained Shortest Path Problem reported in the literature thus far is the LRE-A method of Carlyle et al. (2007), which uses Lagrange Relaxation for preprocessing followed by a bounded search using aggregate constraints. We replace Lagrange Relaxation with the second hybrid method and show that optimal solutions are produced for both network problems with computation times that are between one and two orders of magnitude faster than LRE-A. In addition, these hybrid methods combined with the bounded search are up to 2 orders of magnitude faster than the commercial CPlex package using a straightforward MILP formulation of the problem. Finally, the second hybrid method is used as a preprocessing step on both network problems, prior to running CPlex. This preprocessing reduces the network size sufficiently to allow CPlex to solve all cases to optimality up to 3 orders of magnitude faster than without this preprocessing, and up to an order of magnitude faster than using Lagrange Relaxation for preprocessing. Chapter 1 provides a review of the thesis and some terminology used. Chapter 2 reviews previous approaches to the CSPP, in particular the two current best methods. Chapter 3 applies Lagrange Relaxation to the Submarine Transit Path problem with 2 speeds, to provide a baseline for comparison. The problem is reduced to a single speed, which demonstrates the large duality gap problem possible with Lagrange Relaxation, and the first hybrid method is introduced.
Chapter 4 examines a grid network problem using randomly generated edge costs and weights, and introduces the second hybrid method. Chapter 5 then applies the second hybrid method to both network problems as a preprocessing step, using both CPlex and a bounded search method from the literature to solve to optimality. The conclusion of this thesis and directions for future work are discussed in Chapter 6.
Stoll, Benoît. "Optimisation de Fonctions de Contraste en Séparation de Sources." Toulon, 2000. http://www.theses.fr/2000TOUL0001.
Повний текст джерелаBlind Source Separation aim to recover a set of M independent signals called sources from the observation of N mixtures. Several Source Separation methods exist, most of them are based on Higher Order Statistics. Those methods exploit the source independence hypothesis. Among them we consider the case of the source separation based on contrast function optimization in a spatial linear mixture case. We first propose two contrast families including as a particular case some existing contrasts. Then we determine the optimal solution in a two sources case for this couple of contrast families, thus proposing two algorithms which constitute two classic algorithm generalizations. Then, we study constrained contrast optimization in order to propose algorithms which don't need, as before, data pre-whitening. Two direct constrained optimization method families are considered : the dual methods and the direct methods. Thus we can develop algorithms using Lagrangian concept, penalization concept and a concept of projecting onto the constraint. Com¬puter simulations illustrate the behaviour of the algorithms
Lambert, Pierre-Alain. "Optimisation de formes en aérodynamique : application à la conception des nacelles de moteurs civils." Châtenay-Malabry, Ecole centrale de Paris, 1995. http://www.theses.fr/1995ECAP0420.
Повний текст джерелаКниги з теми "Lagrange optimisation"
Mann, Peter. Constrained Lagrangian Mechanics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198822370.003.0008.
Повний текст джерелаMann, Peter. Matrices. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198822370.003.0031.
Повний текст джерелаЧастини книг з теми "Lagrange optimisation"
Carpentier, Pierre, and Guy Cohen. "Régularisation et Lagrangien augmenté." In Décomposition-coordination en optimisation déterministe et stochastique, 123–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-55428-9_5.
Повний текст джерелаТези доповідей конференцій з теми "Lagrange optimisation"
Rajakaruna, R. M. T. P., W. A. C. Fernando, and J. Calic. "Lagrange-based Video Encoder Optimisation to Enhance Motion Representation in the Compressed-Domain." In 2012 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2012. http://dx.doi.org/10.1109/icme.2012.129.
Повний текст джерелаZhang, Fan, and David R. Bull. "An adaptive Lagrange multiplier determination method for rate-distortion optimisation in hybrid video codecs." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7350883.
Повний текст джерелаFleury, Claude. "Structural Optimization Methods for Large Scale Problems: Status and Limitations." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34326.
Повний текст джерелаKrishnamurthy, Senthil, and Raynitchka Tzoneva. "Comparison of the Lagrange's and Particle Swarm Optimisation solutions of an Economic Emission Dispatch problem with transmission constraints." In 2012 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES). IEEE, 2012. http://dx.doi.org/10.1109/pedes.2012.6484295.
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