Relevant bibliographies by topics / Global and local optimizations

Academic literature on the topic 'Global and local optimizations'

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Published: 25 May 2024

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Journal articles on the topic "Global and local optimizations"

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Ravi Kiran, B., and Jean Serra. "Global–local optimizations by hierarchical cuts and climbing energies." Pattern Recognition 47, no. 1 (January 2014): 12–24. http://dx.doi.org/10.1016/j.patcog.2013.05.012.

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Wang, Wei, Xiaoshan Zhang, and Min Li. "A Filled Function Method Dominated by Filter for Nonlinearly Global Optimization." Journal of Applied Mathematics 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/245427.

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This work presents a filled function method based on the filter technique for global optimization. Filled function method is one of the effective methods for nonlinear global optimization, since it can effectively find a better minimizer. Filter technique is applied to local optimization methods for its excellent numerical results. In order to optimize the filled function method, the filter method is employed for global optimizations in this method. A new filled function is proposed first, and then the algorithm and its properties are proved. The numerical results are listed at the end.
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Li, Shuang, and Qiuwei Li. "Local and Global Convergence of General Burer-Monteiro Tensor Optimizations." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 10266–74. http://dx.doi.org/10.1609/aaai.v36i9.21267.

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Tensor optimization is crucial to massive machine learning and signal processing tasks. In this paper, we consider tensor optimization with a convex and well-conditioned objective function and reformulate it into a nonconvex optimization using the Burer-Monteiro type parameterization. We analyze the local convergence of applying vanilla gradient descent to the factored formulation and establish a local regularity condition under mild assumptions. We also provide a linear convergence analysis of the gradient descent algorithm started in a neighborhood of the true tensor factors. Complementary to the local analysis, this work also characterizes the global geometry of the best rank-one tensor approximation problem and demonstrates that for orthogonally decomposable tensors the problem has no spurious local minima and all saddle points are strict except for the one at zero which is a third-order saddle point.
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Lazzaretto, Andrea, Andrea Toffolo, Matteo Morandin, and Michael R. von Spakovsky. "Criteria for the decomposition of energy systems in local/global optimizations." Energy 35, no. 2 (February 2010): 1157–63. http://dx.doi.org/10.1016/j.energy.2009.06.009.

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Yang, Dezhi, Xintong He, Jun Wang, Guoxian Yu, Carlotta Domeniconi, and Jinglin Zhang. "Federated Causality Learning with Explainable Adaptive Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (March 24, 2024): 16308–15. http://dx.doi.org/10.1609/aaai.v38i15.29566.

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Discovering the causality from observational data is a crucial task in various scientific domains. With increasing awareness of privacy, data are not allowed to be exposed, and it is very hard to learn causal graphs from dispersed data, since these data may have different distributions. In this paper, we propose a federated causal discovery strategy (FedCausal) to learn the unified global causal graph from decentralized heterogeneous data. We design a global optimization formula to naturally aggregate the causal graphs from client data and constrain the acyclicity of the global graph without exposing local data. Unlike other federated causal learning algorithms, FedCausal unifies the local and global optimizations into a complete directed acyclic graph (DAG) learning process with a flexible optimization objective. We prove that this optimization objective has a high interpretability and can adaptively handle homogeneous and heterogeneous data. Experimental results on synthetic and real datasets show that FedCausal can effectively deal with non-independently and identically distributed (non-iid) data and has a superior performance.
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Zatolokin, Y. A., E. I. Vatutin, and V. S. Titov. "ALGORITHMIC OPTIMIZATION OF SOFTWARE IMPLEMENTATION OF ALGORITHMS FOR MULTIPLYING DENSE REAL MATRICES ON GRAPHICS PROCESSORS WITH OPENGL TECHNOLOGY SUPPORT." Proceedings of the Southwest State University 21, no. 5 (October 28, 2017): 6–15. http://dx.doi.org/10.21869/2223-1560-2017-21-5-06-15.

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In the article was given statement of a problem of matrix multiplication. Is is show that desired problem can be simpl formulated but for its solving may be required both heuristic methods and set of algorithmic modifications relating to algorithmic and high-level software optimization taking into account the particular problem and allow to increase the multiplication performance. These include: a comparative analysis of the performance of the actions performed without GPU-specific optimizations and with optimizations, which showed that computations without optimizing the work with global GPU memory have low processing performance. Optimizing data distribution in global and local memory The GPU allows you to reuse the calculation time and increase real performance. To compare the performance of the developed software implementations for OpenGL and CUDA technologies, identical calculations on identical GPUs were performed, which showed higher real performance when using CUDA cores. Specific values of generation performance measured for multi-threaded software implementation on GPU are given for all of described optimizations. It is shown that the most effective approach is based on the method we can get much more performance by technique of caching sub-blocks of the matrices (tiles) in the GPU's on-chip local memory, that with specialized software implementation is provide the performance of 275,3 GFLOP/s for GPU GeForce GTX 960M.
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Bao, Rong, Yongdong Li, Hongguang Wang, and Chunliang Liu. "A Multi-Constrained Optimization Method for THz Backward Wave Oscillators." Applied Sciences 12, no. 20 (October 20, 2022): 10583. http://dx.doi.org/10.3390/app122010583.

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The current design period for various backward wave oscillators (BWOs) is still at least several months. How to find the best structure parameters with an efficient and stable optimization method is a problem facing researchers in both scientific research and engineering work. In this paper, a non-randomized iterative optimization method is proposed. It applies orthogonal design methods to find local solutions that can provide optimal ‘gradient direction’ for several successive next iteration steps. An evaluation function is designed to distinguish the better ones from the local solutions in the multi-constrained optimization of such BWOs. Optimizations from different starting points are performed separately for a global optimal solution. Two BWOs at different frequency ranges are optimized using the proposed method. The validity and stability of the method are verified. It is believed that the method can provide the global optimum and shorten the design period of THz BWOs.
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Tohka, Jussi. "GLOBAL DEFORMABLE SURFACE OPTIMIZATION USING ADAPTIVE CONSTRAINTS AND PENALTIES." Image Analysis & Stereology 24, no. 1 (May 3, 2011): 9. http://dx.doi.org/10.5566/ias.v24.p9-19.

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Deformable models are able to solve surface extraction problems challenged by image noise because imageindependent constraints are used to regularize the shape of the extracted surface. However, this ability of deformable models is shadowed by their application specificity, initialization sensitivity and the difficulty of the selection of proper values for user definable parameters. To overcome these problems restricting the automation of surface extraction, we present a new algorithm, named AdaCoP, for the global minimization of the energy of deformable surfaces. It iteratively performs constrained local minimizations of the energy. It avoids the detection of the same local minimum multiple times by constraining the local optimizations in an adaptive manner. AdaCoP escapes from local minima by imposing an adaptive penalty energy to it. These constraints and penalties prevent the convergence to the local minima already found. The performance of the AdaCoP algorithm is relatively independent on the nature of the underlying image as well as the shape of the surface to be extracted. The performance of the algorithm is evaluated by extracting surfaces from synthetic images. Moreover, the good properties of the algorithm are demonstrated by considering applications within the automated analysis of positron emission tomography images. Although AdaCoP cannot be proven to converge to the global minimum, it is insensitive to its initialization and it therefore provides a way to automate surface extraction problems within medical image analysis.
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Jia, Jia, and Dejun Mu. "Low-Energy-Orientated Resource Scheduling in Cloud Computing by Particle Swarm Optimization." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 2 (April 2018): 339–44. http://dx.doi.org/10.1051/jnwpu/20183620339.

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In order to reduce the energy cost in cloud computing, this paper represents a novel energy-orientated resource scheduling method based on particle swarm optimization. The energy cost model in cloud computing environment is studied first. The optimization of energy cost is then considered as a multiobjective optimization problem, which generates the Pareto optimization set. To solve this multiobjective optimization problem, the particle swarm optimization is involved. The states of one particle consist of both the allocation plan for servers and the frequency plans on servers. Each particle in this algorithm obtains its Pareto local optimization. After the assembly of local optimizations, the algorithm generates the Pareto global optimization for one server plan. The final solution to our problem is the optimal one among all server plans. Experimental results show the good performance of the proposed method. Comparing with the widely-used Round robin scheduling method, the proposed method requires only 45.5% dynamic energy cost.
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Grunbacher, Sophie, Ramin Hasani, Mathias Lechner, Jacek Cyranka, Scott A. Smolka, and Radu Grosu. "On the Verification of Neural ODEs with Stochastic Guarantees." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 11525–35. http://dx.doi.org/10.1609/aaai.v35i13.17372.

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We show that Neural ODEs, an emerging class of time-continuous neural networks, can be verified by solving a set of global-optimization problems. For this purpose, we introduce Stochastic Lagrangian Reachability (SLR), an abstraction-based technique for constructing a tight Reachtube (an over-approximation of the set of reachable states over a given time-horizon), and provide stochastic guarantees in the form of confidence intervals for the Reachtube bounds. SLR inherently avoids the infamous wrapping effect (accumulation of over-approximation errors) by performing local optimization steps to expand safe regions instead of repeatedly forward-propagating them as is done by deterministic reachability methods. To enable fast local optimizations, we introduce a novel forward-mode adjoint sensitivity method to compute gradients without the need for backpropagation. Finally, we establish asymptotic and non-asymptotic convergence rates for SLR.
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Dissertations / Theses on the topic "Global and local optimizations"

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Ding, Baoyan. "A parametric solution for local and global optimization." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq21341.pdf.

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Shiao, Cheng-Mu. "Resolution of kinematic redundancy through local and global optimization /." The Ohio State University, 1988. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487597424137831.

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Wang, Le Yi. "Normed double algebras, local and global optimization, and slow H00 adaptation." Thesis, McGill University, 1989. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=74258.

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A normed space of input-output mappings equipped with two products, one global and the other local, called a normed double algebra (NDA), is introduced for the frozen-time analysis of stabilization and optimization of a class of slowly time-varying systems. Local-global relations within a normed double algebra are established, in the time and frequency domains, in systems which vary slowly. The local-global relations, applied to system properties such as stability, coprime factorization and optimization, enable global properties to be deduced from the local ones, especially in the frequency domain, by methods which are computationally tractable, at least in principle. Classical frozen-time stability is reinterpreted in terms of a relation between local and global resolvents in the NDA. Relations between local and global coprime factorizations and their implications to local and global robust stability are obtained.
An explicit double algebraic expression for adaptive BIBO sensitivity reduction is established. Notions of adaptive and robust (non-adaptive) sensitivity minimization are applied to an example involving rejection of narrowband disturbances of uncertain bandwidth and center frequency. The double algebra symbolism is employed to show that adaptive minimization can give better sensitivity than $H sp infty$ optimal robust minimization.
To implement a design strategy of global sensitivity optimization using local $H sp infty$ interpolation, Lipschitz continuity of optimal $H sp infty$ interpolants on data is investigated. While optimal $H sp infty$ interpolants in general do not depend Lipschitz continuously on data, $ delta$-suboptimal interpolants based on AAK's maximal entropy solutions satisfy an appropriate Lipschitz continuity condition. These, applied to slowly time-varying systems, achieve approximations to the globally optimal interpolants, which become accurate as the rates of variation approach zero.
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Ragon, Scott Alan II. "Development of a Global/Local Approach and a Geometrically Non-linear Local Panel Analysis for Structural Design." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30761.

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A computationally efficient analysis capability for the geometrically non-linear response of compressively loaded prismatic plate structures was developed. Both a "full" finite strip solution procedure and a "reduced" solution procedure were implemented in a FORTRAN 90 computer code, and comparisons were made with results available in the technical literature. Both the full and reduced solution procedures were demonstrated to provide accurate results for displacement and strain quantities through moderately large post-buckling loads. The full method is a non-linear finite strip analysis of the semi-analytical, multi-term type. Individual finite strips are modeled as balanced and symmetric laminated composite materials which are assumed to behave orthotropically in bending, and the structure is loaded in uniaxial or biaxial compression. The loaded ends of the structure are assumed to be simply supported, and geometric shape imperfections may be modeled. The reduced solution method makes use of a reduced basis technique in conjunction with the full finite strip analysis. Here, the potentially large set of non-linear algebraic equations produced by the finite strip method are replaced by a small set of system equations. In the present implementation, the basis vectors consist of successive derivatives of the non-linear solution vector with respect to a loading parameter. Depending on the nature of the problem, the reduced solution procedure is capable of computational savings of up to 60%+ compared to the full finite strip method. The reduced method is most effective in reducing the computational cost of the full method when the most significant portion of the cost of the full method is factorization of the assembled system matrices. The robustness and efficiency of the reduced solution procedure was found to be sensitive to the user specified error norm which is used during the reduced solution procedure to determine when to generate new sets of basis vectors. In parallel with this effort, a new method for performing global/local design optimization of large complex structures (such as aircraft wings or fuselages) was developed. A simple and flexible interface between the global and local design levels was constructed using response surface methodology. The interface is constructed so as to minimize the changes required in either the global design code or the local design codes(s). Proper coupling is maintained between the global and local design levels via a "weight constraint" and the transfer of global stiffness information to the local level. The method was verified using a simple isotropic global wing model and the local panel design code PASCO.
Ph. D.
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Huynh, Vu Anh. "Combining local and global optimization for planning and control in information space." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45281.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008.
Includes bibliographical references (leaves 99-102).
This thesis presents a novel algorithm, called the parametric optimized belief roadmap (POBRM), to address the problem of planning a trajectory for controlling a robot with imperfect state information under uncertainty. This question is formulated abstractly as a partially observable stochastic shortest path (POSSP) problem. We assume that the feature-based map of a region is available to assist the robot's decision-making. The POBRM is a two-phase algorithm that combines local and global optimization. In an offline phase, we construct a belief graph by probabilistically sampling points around the features that potentially provide the robot with valuable information. Each edge of the belief graph stores two transfer functions to predict the cost and the conditional covariance matrix of a final state estimate if the robot follows this edge given an initial mean and covariance. In an online phase, a sub-optimal trajectory is found by the global Dijkstra's search algorithm, which ensures the balance between exploration and exploitation. Moreover, we use the iterative linear quadratic Gaussian algorithm (iLQG) to find a locally-feedback control policy in continuous state and control spaces to traverse the sub-optimal trajectory. We show that, under some suitable technical assumptions, the error bound of a sub-optimal cost compared to the globally optimal cost can be obtained. The POBRM algorithm is not only robust to imperfect state information but also scalable to find a trajectory quickly in high-dimensional systems and environments. In addition, the POBRM algorithm is capable of answering multiple queries efficiently. We also demonstrate performance results by 2D simulation of a planar car and 3D simulation of an autonomous helicopter.
by Vu Anh Huynh.
S.M.
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Gogolashvili, Davit. "Global and local Kernel methods for dataset shift, scalable inference and optimization." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS363v2.pdf.

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Dans de nombreux problèmes du monde réel, les données de formation et les données de test ont des distributions différentes. Cette situation est communément appelée " décalage de l'ensemble de données ". Les paramètres les plus courants pour le décalage des ensembles de données souvent considérés dans la littérature sont le décalage des covariables et le décalage des cibles. Dans cette thèse, nous étudions les modèles nonparamétriques appliqués au scénario de changement d'ensemble de données. Nous développons un nouveau cadre pour accélérer la régression par processus gaussien. En particulier, nous considérons des noyaux de localisation à chaque point de données pour réduire les contributions des autres points de données éloignés, et nous dérivons le modèle GPR découlant de l'application de cette opération de localisation. Grâce à une série d'expériences, nous démontrons la performance compétitive de l'approche proposée par rapport au GPR complet, à d'autres modèles localisés et aux processus gaussiens profonds. De manière cruciale, ces performances sont obtenues avec des accélérations considérables par rapport au GPR global standard en raison de l'effet de sparsification de la matrice de Gram induit par l'opération de localisation. Nous proposons une nouvelle méthode pour estimer le minimiseur et la valeur minimale d'une fonction de régression lisse et fortement convexe à partir d'observations contaminées par du bruit aléatoire
In many real world problems, the training data and test data have different distributions. The most common settings for dataset shift often considered in the literature are covariate shift and target shift. In this thesis, we investigate nonparametric models applied to the dataset shift scenario. We develop a novel framework to accelerate Gaussian process regression. In particular, we consider localization kernels at each data point to down-weigh the contributions from other data points that are far away, and we derive the GPR model stemming from the application of such localization operation. We propose a new method for estimating the minimizer and the minimum value of a smooth and strongly convex regression function from the observations contaminated by random noise
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Steffens, Michael J. "A combined global and local methodology for launch vehicle trajectory design-space exploration and optimization." Thesis, Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51884.

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Trajectory optimization is an important part of launch vehicle design and operation. With the high costs of launching payload into orbit, every pound that can be saved increases affordability. One way to save weight in launch vehicle design and operation is by optimizing the ascent trajectory. Launch vehicle trajectory optimization is a field that has been studied since the 1950’s. Originally, analytic solutions were sought because computers were slow and inefficient. With the advent of computers, however, different algorithms were developed for the purpose of trajectory optimization. Computer resources were still limited, and as such the algorithms were limited to local optimization methods, which can get stuck in specific regions of the design space. Local methods for trajectory optimization have been well studied and developed. Computer technology continues to advance, and in recent years global optimization has become available for application to a wide variety of problems, including trajectory optimization. The aim of this thesis is to create a methodology that applies global optimization to the trajectory optimization problem. Using information from a global search, the optimization design space can be reduced and a much smaller design space can be analyzed using already existing local methods. This allows for areas of interest in the design space to be identified and further studied and helps overcome the fact that many local methods can get stuck in local optima. The design space included in trajectory optimization is also considered in this thesis. The typical optimization variables are initial conditions and flight control variables. For direct optimization methods, the trajectory phase structure is currently chosen a priori. Including trajectory phase structure variables in the optimization process can yield better solutions. The methodology and phase structure optimization is demonstrated using an earth-to-orbit trajectory of a Delta IV Medium launch vehicle. Different methods of performing the global search and reducing the design space are compared. Local optimization is performed using the industry standard trajectory optimization tool POST. Finally, methods for varying the trajectory phase structure are presented and the results are compared.
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Paudel, Danda Pani. "Local and global methods for registering 2D image sets and 3D point clouds." Thesis, Dijon, 2015. http://www.theses.fr/2015DIJOS077/document.

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Pas de résumé
In this thesis, we study the problem of registering 2D image sets and 3D point clouds under threedifferent acquisition set-ups. The first set-up assumes that the image sets are captured using 2Dcameras that are fully calibrated and coupled, or rigidly attached, with a 3D sensor. In this context,the point cloud from the 3D sensor is registered directly to the asynchronously acquired 2D images.In the second set-up, the 2D cameras are internally calibrated but uncoupled from the 3D sensor,allowing them to move independently with respect to each other. The registration for this set-up isperformed using a Structure-from-Motion reconstruction emanating from images and planar patchesrepresenting the point cloud. The proposed registration method is globally optimal and robust tooutliers. It is based on the theory Sum-of-Squares polynomials and a Branch-and-Bound algorithm.The third set-up consists of uncoupled and uncalibrated 2D cameras. The image sets from thesecameras are registered to the point cloud in a globally optimal manner using a Branch-and-Prunealgorithm. Our method is based on a Linear Matrix Inequality framework that establishes directrelationships between 2D image measurements and 3D scene voxels
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Cafieri, Sonia. "From local to global and back : a closed walk in mathematical programming and its applications." Habilitation à diriger des recherches, Université Paul Sabatier - Toulouse III, 2012. http://tel.archives-ouvertes.fr/tel-00979273.

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Ce document propose un parcours de mes travaux de recherche en optimisation, en passant par l'optimisation mixte en variables entières, l'optimisation non-linéaire continue locale et le clustering dans les réseaux (graphes). Le premier chapitre traite de la programmation non linéaire mixte en variables entières et de l'optimisation globale déterministe. Il présente des contributions relatives à des investigations théoriques ainsi que des applications à des problèmes concrets. Nous discutons principalement de relaxations convexes et de reformulations automatiques de problèmes de programmation mathématique, dans le but d'améliorer l'efficacité des algorithmes de Branch-and-Bound. Dans le cadre de la programmation polynomiale, nous avons étudié des relaxations convexes pour les monômes multilinéaires et la génération de relaxations compactes de problèmes polynomiaux basés sur une technique spécifique de reformulation-linéarisation (RLT). Parmi les applications, une attention particulière est portée à des problèmes qui se posent dans la gestion du trafic aérien. Nous avons proposé de nouveaux modèles mathématiques et des approches de résolution basées d'une part sur l'optimisation mixte en variables entières et d'autre part sur le contrôle optimal. Deux thèmes de l'optimisation continue non-linéaire sont décrits au deuxième chapitre. Des méthodes de point intérieur pour la programmation quadratique et leurs noyaux d'algèbre linéaire (systèmes KKT) sont d'abord discutées. L'accent est mis sur les méthodes itératives pour les systèmes KKT et sur des questions connexes, telles que les techniques de préconditionnement et les propriétés de convergence. L'autre sujet discuté concerne, encore une fois, des problèmes de trafic aérien. Il porte sur les approches déjà mentionnées de contrôle optimal qui conduisent à des problèmes non-linéaires. Le troisième chapitre présente mes principaux résultats dans le domaine du clustering dans les réseaux. Le problème de l'identification de clusters dans les réseaux peut être formulé en utilisant la programmation mathématique et conduit généralement à un problème d'optimisation combinatoire. Mes contributions concernent les critères de classification et les méthodes de clustering correspondantes. Une attention particulière est portée aux méthodes exactes utilisées pour résoudre l'ensemble du problème d'optimisation ou, localement, les sous-problèmes survenant dans des heuristiques hiérarchiques, ou enfin dans le raffinement des solutions obtenues précédemment par d'autres méthodes.
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Seresta, Omprakash. "Buckling, Flutter, and Postbuckling Optimization of Composite Structures." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/26401.

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This research work deals with the design and optimization of a large composite structure. In design of large structural systems, it is customary to divide the problem into many smaller independent/semi-independent local design problems. For example, the wing structure design problem is decomposed into several local panel design problem. The use of composite necessitates the inclusion of ply angles as design variables. These design variables are discrete in nature because of manufacturing constraint. The multilevel approach results into a nonblended solution with no continuity of laminate layups across the panels. The nonblended solution is not desirable because of two reasons. First, the structural integrity of the whole system is questionable. Second, even if there is continuity to some extent, the manufacturing process ends up being costlier. In this work, we develop a global local design methodology to design blended composite laminates across the whole structural system. The blending constraint is imposed via a guide based approach within the genetic algorithm optimization scheme. Two different blending schemes are investigated, outer and inner blending. The global local approach is implemented for a complex composite wing structure design problem, which is known to have a strong global local coupling. To reduce the computational cost, the originally proposed local one dimensional search is replaced by an intuitive local improvement operator. The local panels design problem arises in global/local wing structure design has a straight edge boundary condition. A postbuckling analysis module is developed for such panels with applied edge displacements. A parametric study of the effects of flexural and inplane stiffnesses on the design of composite laminates for optimal postbuckling performance is done. The design optimization of composite laminates for postbuckling strength is properly formulated with stacking sequence as design variables. Next, we formulate the stacking sequence design (fiber orientation angle of the layers) of laminated composite flat panels for maximum supersonic flutter speed and maximum thermal buckling capacity. The design is constrained so that the behavior of the panel in the vicinity of the flutter boundary should be limited to stable limit cycle oscillation. A parametric study is carried out to investigate the tradeoff between designs for thermal buckling and flutter. In an effort to include the postbuckling constraint into the multilevel design optimization of large composite structure, an alternative cheap methodology for predicting load paths in postbuckled structure is presented. This approach being computationally less expensive compared to full scale nonlinear analysis can be used in conjunction with an optimizer for preliminary design of large composite structure with postbuckling constraint. This approach assumes that the postbuckled stiffness of the structure, though reduced considerably, remains linear. The analytical expressions for postbuckled stiffness are given in a form that can be used with any commercially available linear finite element solver. Using the developed approximate load path prediction scheme, a global local design approach is developed to design large composite structure with blending and local postbuckling constraints. The methodology is demonstrated via a composite wing box design with blended laminates.
Ph. D.
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Books on the topic "Global and local optimizations"

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Migdalas, Athanasios, Panos M. Pardalos, and Peter Värbrand, eds. From Local to Global Optimization. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-5284-7.

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Athanasios, Migdalas, Pardalos P. M. 1954-, and Värbrand Peter, eds. From local to global optimization. Dordrecht: Kluwer Academic, 2001.

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M, Pardalos Panos, and Värbrand Peter, eds. From Local to Global Optimization. Boston, MA: Springer US, 2001.

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Athanasios, Migdalas, Pardalos P. M. 1954-, and Värbrand Peter, eds. From local to global optimization. Dordrecht: Kluwer Academic, 2001.

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Floudas, Christodoulos A., Pãnos M. Pardalos, Claire S. Adjiman, William R. Esposito, Zeynep H. Gümüş, Stephen T. Harding, John L. Klepeis, Clifford A. Meyer, and Carl A. Schweiger. Handbook of Test Problems in Local and Global Optimization. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-3040-1.

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Floudas, Christodoulos A. Handbook of Test Problems in Local and Global Optimization. Boston, MA: Springer US, 1999.

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A, Floudas Christodoulos, ed. Handbook of test problems in local and global optimization. Dordrecht: Kluwer Academic Publishers, 1999.

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A, Floudas Christodoulos, and Pardalos P. M. 1954-, eds. Optimization in computational chemistry and molecular biology: Local and global approaches. Dordrecht: Kluwer Academic Publishers, 2000.

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Mirando, Wander Melo. Local/global. São Paulo: Fundaço Memorial de América Latina, 1998.

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Berman, Ila. URBANbuild: Local global. Richmond, CA: William Stout Publishers, 2008.

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Book chapters on the topic "Global and local optimizations"

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Schäffler, Stefan. "Unconstrained Local Optimization." In Global Optimization, 7–19. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3927-1_2.

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Svanberg, K. "Local and Global Optima." In Optimization of Large Structural Systems, 579–88. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-010-9577-8_28.

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Du, Ding-Zhu, Panos M. Pardalos, and Weili Wu. "From Local to Global." In Nonconvex Optimization and Its Applications, 227–43. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-5795-8_15.

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El-Shorbagy, M. A., and Aboul Ella Hassanien. "Spherical Local Search for Global Optimization." In Advances in Intelligent Systems and Computing, 304–12. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99010-1_28.

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Krzeszowski, Tomasz, Bogdan Kwolek, and Konrad Wojciechowski. "Model-Based 3D Human Motion Capture Using Global-Local Particle Swarm Optimizations." In Computer Recognition Systems 4, 297–306. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20320-6_31.

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Nareyek, Alexander. "Using global constraints for local search." In Constraint Programming and Large Scale Discrete Optimization, 9–28. Providence, Rhode Island: American Mathematical Society, 2001. http://dx.doi.org/10.1090/dimacs/057/02.

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Bolton, Hermanus P. J., Jaco F. Schutte, and Albert A. Groenwold. "Multiple Parallel Local Searches in Global Optimization." In Recent Advances in Parallel Virtual Machine and Message Passing Interface, 88–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45255-9_15.

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Bagirov, A. M., N. Sultanova, A. Al Nuaimat, and S. Taheri. "Solving Minimax Problems: Local Smoothing Versus Global Smoothing." In Numerical Analysis and Optimization, 23–43. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90026-1_2.

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Hamma, Beidi Soumana. "Local and Global Behavior of Moving Polytope Algorithms." In Nonconvex Optimization and Its Applications, 305–25. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-5284-7_14.

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Hendrix, Eligius M. T., and Ana Maria A. C. Rocha. "On Local Convergence of Stochastic Global Optimization Algorithms." In Computational Science and Its Applications – ICCSA 2021, 456–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86976-2_31.

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Abstract:
AbstractIn engineering optimization with continuous variables, the use of Stochastic Global Optimization (SGO) algorithms is popular due to the easy availability of codes. All algorithms have a global and local search character, where the global behaviour tries to avoid getting trapped in local optima and the local behaviour intends to reach the lowest objective function values. As the algorithm parameter set includes a final convergence criterion, the algorithm might be running for a while around a reached minimum point. Our question deals with the local search behaviour after the algorithm reached the final stage. How fast do practical SGO algorithms actually converge to the minimum point? To investigate this question, we run implementations of well known SGO algorithms in a final local phase stage.
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Conference papers on the topic "Global and local optimizations"

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Renaud, J. E., and G. A. Gabriele. "Sequential Global Approximation in Non-Hierarchic System Decomposition and Optimization." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0086.

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Abstract A procedure for the optimization of non-hierarchic systems by decomposition into reduced subspaces is presented. Sequential global approximation is proposed as a coordination procedure for subspace optimizations. The same objective function and cumulative constraints are imposed at each subspace. Non-local functions are approximated at the subspaces using global sensitivities. The method optimizes the subspace problems concurrently allowing for parallel processing. Following each sequence of concurrent subspace optimizations an approximation to the global problem is formed using design data accumulated during the subspace optimizations. The solution of the global approximation problem is used as the starting point for subsequent subspace optimizations in an iterative solution procedure. Preliminary studies on two engineering design examples illustrate the methods potential.
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Zhou Wu, Lu Xu, Tommy W. S. Chow, and Mingbo Zhao. "Local cooperation delivers global optimization." In 2012 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2012. http://dx.doi.org/10.1109/cec.2012.6256548.

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Alam, Mohammad Shafiul, Md Ashiqur Rahman, and Md Monirul Islam. "A new framework for balancing both local and global optimizations in evolutionary algorithms." In 2007 10th International Conference on Computer and Information Technology (ICCIT 2007). IEEE, 2007. http://dx.doi.org/10.1109/iccitechn.2007.4579358.

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Xun Wang, Chang-Dong Wang, and Jian-Huang Lai. "Modularity optimization by Global-Local Search." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727287.

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Liu, Qiang, Sameer B. Mulani, and Rakesh K. Kapania. "Global/Local Multidisciplinary Design Optimization of Subsonic Wing." In 10th AIAA Multidisciplinary Design Optimization Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2014. http://dx.doi.org/10.2514/6.2014-0471.

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Kuszmaul, Christopher L. "FFT communications requirement optimizations on massively parallel architectures with local and global interprocessor communications capabilities." In San Diego '90, 8-13 July, edited by Franklin T. Luk. SPIE, 1990. http://dx.doi.org/10.1117/12.23497.

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Lancinskas, Algirdas, Julius Zilinskas, and Pilar Martinez Ortigosa. "Local optimization in global Multi-Objective Optimization Algorithms." In 2011 Third World Congress on Nature and Biologically Inspired Computing (NaBIC). IEEE, 2011. http://dx.doi.org/10.1109/nabic.2011.6089613.

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Ghommem, Mehdi, Muhammad Hajj, Bret Stanford, Layne Watson, and Philip Beran. "Global and Local Optimization of Flapping Kinematics." In 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
20th AIAA/ASME/AHS Adaptive Structures Conference
14th AIAA. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-1983.

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Ding, Quanxin, and Hua Liu. "Development in local and global optimization methods." In Photonics Asia 2007, edited by Yongtian Wang, Theo T. Tschudi, Jannick P. Rolland, and Kimio Tatsuno. SPIE, 2007. http://dx.doi.org/10.1117/12.756511.

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Mengistu, Temesgen, Wahid Ghaly, and Tarek Mansour. "Global- and Local-Shape Aerodynamic Optimization of Turbine Blades." In 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-6933.

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Reports on the topic "Global and local optimizations"

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Haftka, Raphael T. Theory and Algorithms for Global/Local Design Optimization. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada437353.

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Gurdal, Zafer, Scott Ragon, and Douglas Lindner. Global/Local Design Optimization of a Power Distribution System. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada387344.

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Gurdal, Zafer, Scott Ragon, and Douglas Lindner. Global/Local Design Optimization of A Power Distribution System. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada389411.

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Lindner, Douglas K. Energy Based Topology Optimization of Morphing Wings a Multidisciplinary Global/Local Design Approach. Fort Belvoir, VA: Defense Technical Information Center, December 2006. http://dx.doi.org/10.21236/ada480198.

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Aceves, S., V. Sahai, and W. Stein. Explanation of how to run the global local optimization code (GLO) to find surface heat flux. Office of Scientific and Technical Information (OSTI), March 1999. http://dx.doi.org/10.2172/8214.

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Chuang, Feng-Chuan. Global structual optimizations of surface systems with a genetic algorithm. Office of Scientific and Technical Information (OSTI), January 2005. http://dx.doi.org/10.2172/850036.

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Cooper, Leon N. Local and Global Factors in Learning. Fort Belvoir, VA: Defense Technical Information Center, November 1985. http://dx.doi.org/10.21236/ada161128.

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Sánchez Gassen, Nora, Oskar Penje, and Elin Slätmo. Global goals for local priorities: The 2030 Agenda at local level. Nordregio, September 2018. http://dx.doi.org/10.30689/r2018:2.1403-2503.

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Chandrasekhar, Arun, Melanie Morten, and Alessandra Peter. Network-Based Hiring: Local Benefits; Global Costs. Cambridge, MA: National Bureau of Economic Research, February 2020. http://dx.doi.org/10.3386/w26806.

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Durlauf, Steven, and Paul Johnson. Local Versus Global Convergence Across National Economies. Cambridge, MA: National Bureau of Economic Research, February 1992. http://dx.doi.org/10.3386/w3996.

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