Дисертації з теми "Convex constrained optimization"
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Shewchun, John Marc 1972. "Constrained control using convex optimization." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/46471.
Повний текст джерелаYang, Yi. "Sequential convex approximations of chance constrained programming /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?IELM%202008%20YANG.
Повний текст джерелаLintereur, Beau V. (Beau Vincent) 1973. "Constrained H̳₂ design via convex optimization with applications." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50628.
Повний текст джерелаIn title on t.p., double-underscored "H" appears in script.
Includes bibliographical references (p. 133-138).
A convex optimization controller design method is presented which minimizes the closed-loop H2 norm, subject to constraints on the magnitude of closed-loop transfer functions and transient responses due to specified inputs. This method uses direct parameter optimization of the closed-loop Youla or Q-parameter where the variables are the coefficients of a stable orthogonal basis. The basis is constructed using the recently rediscovered Generalized Orthonormal Basis Functions (GOBF) that have found application in system identification. It is proposed that many typical control specifications including robustness to modeling error and gain and phase margins can be posed with two simple constraints in the frequency and time domain. With some approximation, this formulation allows the controller design problem to be cast as a quadratic program. Two example applications demonstrate the practical utility of this method for real systems. First this method is applied to the roll axis of the EOS-AM1 spacecraft attitude control system, with a set of performance and robustness specifications. The constrained H2 controller simultaneously meets the specifications where previous model-based control studies failed. Then a constrained H2 controller is designed for an active vibration isolation system for a spaceborne optical technology demonstration test stand. Mixed specifications are successfully incorporated into the design and the results are verified with experimental frequency data.
by Beau V. Lintereur.
S.M.
Roese-Koerner, Lutz [Verfasser]. "Convex Optimization for Inequality Constrained Adjustment Problems / Lutz Roese-Koerner." Bonn : Universitäts- und Landesbibliothek Bonn, 2015. http://d-nb.info/1078728534/34.
Повний текст джерелаOliveira, Rafael Massambone de. "String-averaging incremental subgradient methods for constrained convex optimization problems." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-14112017-150512/.
Повний текст джерелаNesta tese de doutorado, propomos novos métodos iterativos para a solução de uma classe de problemas de otimização convexa. Em geral, consideramos problemas nos quais a função objetivo é composta por uma soma finita de funções convexas e o conjunto de restrições é, pelo menos, convexo e fechado. Os métodos iterativos que propomos são criados, basicamente, através da junção de métodos de subgradientes incrementais e do algoritmo de média das sequências. Além disso, visando obter métodos flexíveis para soluções de problemas de otimização com muitas restrições (e possivelmente em altas dimensões), dadas em geral por funções convexas, a nossa análise inclui um operador que calcula projeções aproximadas sobre o conjunto viável, no lugar da projeção Euclideana. Essa característica é empregada nos dois métodos que propomos; um determinístico e o outro estocástico. Uma análise de convergência é proposta para ambos os métodos e experimentos numéricos são realizados a fim de verificar a sua aplicabilidade, principalmente em problemas de grande escala.
Li, Yusong. "Stochastic maximum principle and dynamic convex duality in continuous-time constrained portfolio optimization." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/45536.
Повний текст джерелаKůdela, 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.
Повний текст джерелаGünther, Christian [Verfasser]. "On generalized-convex constrained multi-objective optimization and application in location theory / Christian Günther." Halle, 2018. http://d-nb.info/1175950602/34.
Повний текст джерелаWang, Guanglei. "Relaxations in mixed-integer quadratically constrained programming and robust programming." Thesis, Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0026/document.
Повний текст джерелаMany real life problems are characterized by making decisions with current information to achieve certain objectives. Mathematical programming has been developed as a successful tool to model and solve a wide range of such problems. However, many seemingly easy problems remain challenging. And some easy problems such as linear programs can be difficult in the face of uncertainty. Motivated by a telecommunication problem where assignment decisions have to be made such that the cloud virtual machines are assigned to servers in a minimum-cost way, we employ several mathematical programming tools to solve the problem efficiently and develop new tools for general theoretical problems. In brief, our work can be summarized as follows. We provide an exact formulation and several reformulations on the cloud virtual machine assignment problem. Then several valid inequalities are used to strengthen the exact formulation, thereby accelerating the solution procedure significantly. In addition, an effective Lagrangian decomposition is proposed. We show that, the bounds providedby the proposed Lagrangian decomposition is strong, both theoretically and numerically. Finally, a symmetry-induced model is proposed which may reduce a large number of bilinear terms in some special cases. Motivated by the virtual machine assignment problem, we also investigate a couple of general methods on the approximation of convex and concave envelopes for bilinear optimization over a hypercube. We establish several theoretical connections between different techniques and prove the equivalence of two seeming different relaxed formulations. An interesting research direction is also discussed. To address issues of uncertainty, a novel paradigm on general linear problems with uncertain parameters are proposed. This paradigm, termed as multipolar robust optimization, generalizes notions of static robustness, affinely adjustable robustness, fully adjustable robustness and fills the gaps in-between. As consequences of this new paradigms, several known results are implied. Further, we prove that the multipolar approach can generate a sequence of upper bounds and a sequence of lower bounds at the same time and both sequences converge to the robust value of fully adjustable robust counterpart under some mild assumptions
Blomqvist, Anders. "A convex optimization approach to complexity constrained analytic interpolation with applications to ARMA estimation and robust control." Doctoral thesis, Stockholm, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-117.
Повний текст джерелаSapankevych, Nicholas. "Constrained Motion Particle Swarm Optimization for Non-Linear Time Series Prediction." Scholar Commons, 2015. https://scholarcommons.usf.edu/etd/5569.
Повний текст джерелаNguyen, Ngoc Anh. "Explicit robust constrained control for linear systems : analysis, implementation and design based on optimization." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLC012/document.
Повний текст джерелаPiecewise affine (PWA) feedback control laws have received significant attention due to their relevance for the control of constrained systems, hybrid systems; equally for the approximation of nonlinear control. However, they are associated with serious implementation issues. Motivated from the interest in this class of particular controllers, this thesis is mostly related to their analysis and design.The first part of this thesis aims to compute the robustness and fragility margins for a given PWA control law and a linear discrete-time system. More precisely, the robustness margin is defined as the set of linear time-varying systems such that the given PWA control law keeps the trajectories inside a given feasible set. On a different perspective, the fragility margin contains all the admissible variations of the control law coefficients such that the positive invariance of the given feasible set is still guaranteed. It will be shown that if the given feasible set is a polytope, then so are these robustness/fragility margins.The second part of this thesis focuses on inverse optimality problem for the class of PWA controllers. Namely, the goal is to construct an optimization problem whose optimal solution is equivalent to the given PWA function. The methodology is based on emph convex lifting: an auxiliary 1− dimensional variable which enhances the convexity characterization into recovered optimization problem. Accordingly, if the given PWA function is continuous, the optimal solution to this reconstructed optimization problem will be shown to be unique. Otherwise, if the continuity of this given PWA function is not fulfilled, this function will be shown to be one optimal solution to the recovered problem.In view of applications in linear model predictive control (MPC), it will be shown that any continuous PWA control law can be obtained by a linear MPC problem with the prediction horizon at most equal to 2 prediction steps. Aside from the theoretical meaning, this result can also be of help to facilitate implementation of PWA control laws by avoiding storing state space partition. Another utility of convex liftings will be shown in the last part of this thesis to be a control Lyapunov function. Accordingly, this convex lifting will be deployed in the so-called robust control design based on convex liftings for linear system affected by bounded additive disturbances and polytopic uncertainties. Both implicit and explicit controllers can be obtained. This method can also guarantee the recursive feasibility and robust stability. However, this control Lyapunov function is only defined over the maximal λ −contractive set for a given 0 ≤ λ < 1 which is known to be smaller than the maximal controllable set. Therefore, an extension of the above method to the N-steps controllable set will be presented. This method is based on a cascade of convex liftings where an auxiliary variable will be used to emulate a Lyapunov function. Namely, this variable will be shown to be non-negative, to strictly decrease for N first steps and to stay at 0 afterwards. Accordingly, robust stability is sought
Fleming, James. "Robust and stochastic MPC of uncertain-parameter systems." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:c19ff07c-0756-45f6-977b-9d54a5214310.
Повний текст джерелаLuedtke, James. "Integer Programming Approaches for Some Non-convex and Stochastic Optimization Problems." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19711.
Повний текст джерелаEl, Gamal Mostafa. "Distributed Statistical Learning under Communication Constraints." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/314.
Повний текст джерелаLi, Nan. "Maximum Likelihood Identification of an Information Matrix Under Constraints in a Corresponding Graphical Model." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/128.
Повний текст джерелаVonwirth, Christian [Verfasser], and Jörn [Akademischer Betreuer] Sass. "Continuous-Time Portfolio Optimization under Partial Information and Convex Constraints: Deriving Explicit Results / Christian Vonwirth ; Betreuer: Jörn Sass." Kaiserslautern : Technische Universität Kaiserslautern, 2017. http://d-nb.info/1137206500/34.
Повний текст джерелаHeinrich, André. "Fenchel duality-based algorithms for convex optimization problems with applications in machine learning and image restoration." Doctoral thesis, Universitätsbibliothek Chemnitz, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-108923.
Повний текст джерелаStühmer, Jan [Verfasser], Daniel [Akademischer Betreuer] [Gutachter] Cremers, and William T. [Gutachter] Freeman. "A Convex Optimization Framework for Connectivity Constraints in Image Segmentation and 3D Reconstruction / Jan Stühmer ; Gutachter: Daniel Cremers, William T. Freeman ; Betreuer: Daniel Cremers." München : Universitätsbibliothek der TU München, 2016. http://d-nb.info/1131253671/34.
Повний текст джерелаNagamune, Ryozo. "Robust Control with Complexity Constraint : A Nevanlinna-Pick Interpolation Approach." Doctoral thesis, KTH, Mathematics, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3394.
Повний текст джерелаFuentes, Marc. "Analyse et optimisation de problèmes sous contraintes d'autocorrélation." Phd thesis, Université Paul Sabatier - Toulouse III, 2007. http://tel.archives-ouvertes.fr/tel-00195013.
Повний текст джерелаProdan, Ionela. "Control of Multi-Agent Dynamical Systems in the Presence of Constraints." Thesis, Supélec, 2012. http://www.theses.fr/2012SUPL0019/document.
Повний текст джерелаThe goal of this thesis is to propose solutions for the optimal control of multi-agent dynamical systems under constraints. Elements from control theory and optimization are merged together in order to provide useful tools which are further applied to different problems involving multi-agent formations. The thesis considers the challenging case of agents subject to dynamical constraints. To deal with these issues, well established concepts like set-theory, differential flatness, Model Predictive Control (MPC), Mixed-Integer Programming (MIP) are adapted and enhanced. Using these theoretical notions, the thesis concentrates on understanding the geometrical properties of the multi-agent group formation and on providing a novel synthesis framework which exploits the group structure. In particular, the formation design and the collision avoidance conditions are casted as geometrical problems and optimization-based procedures are developed to solve them. Moreover, considerable advances in this direction are obtained by efficiently using MIP techniques (in order to derive an efficient description of the non-convex, non-connected feasible region which results from multi-agent collision and obstacle avoidance constraints) and stability properties (in order to analyze the uniqueness and existence of formation configurations). Lastly, some of the obtained theoretical results are applied on a challenging practical application. A novel combination of MPC and differential flatness (for reference generation) is used for the flight control of Unmanned Aerial Vehicles (UAVs)
Flammarion, Nicolas. "Stochastic approximation and least-squares regression, with applications to machine learning." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE056/document.
Повний текст джерелаMany problems in machine learning are naturally cast as the minimization of a smooth function defined on a Euclidean space. For supervised learning, this includes least-squares regression and logistic regression. While small problems are efficiently solved by classical optimization algorithms, large-scale problems are typically solved with first-order techniques based on gradient descent. In this manuscript, we consider the particular case of the quadratic loss. In the first part, we are interestedin its minimization when its gradients are only accessible through a stochastic oracle. In the second part, we consider two applications of the quadratic loss in machine learning: clustering and estimation with shape constraints. In the first main contribution, we provided a unified framework for optimizing non-strongly convex quadratic functions, which encompasses accelerated gradient descent and averaged gradient descent. This new framework suggests an alternative algorithm that exhibits the positive behavior of both averaging and acceleration. The second main contribution aims at obtaining the optimal prediction error rates for least-squares regression, both in terms of dependence on the noise of the problem and of forgetting the initial conditions. Our new algorithm rests upon averaged accelerated gradient descent. The third main contribution deals with minimization of composite objective functions composed of the expectation of quadratic functions and a convex function. Weextend earlier results on least-squares regression to any regularizer and any geometry represented by a Bregman divergence. As a fourth contribution, we consider the the discriminative clustering framework. We propose its first theoretical analysis, a novel sparse extension, a natural extension for the multi-label scenario and an efficient iterative algorithm with better running-time complexity than existing methods. The fifth main contribution deals with the seriation problem. We propose a statistical approach to this problem where the matrix is observed with noise and study the corresponding minimax rate of estimation. We also suggest a computationally efficient estimator whose performance is studied both theoretically and experimentally
Lorenz, Nicole. "Application of the Duality Theory." Doctoral thesis, Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-94108.
Повний текст джерелаTall, Abdoulaye. "Optimisation et Auto-Optimisation dans les réseaux LTE." Thesis, Avignon, 2015. http://www.theses.fr/2015AVIG0208/document.
Повний текст джерелаThe mobile network of Orange in France comprises more than 100 000 2G, 3G and 4G antennas with severalfrequency bands, not to mention many femto-cells for deep-indoor coverage. These numbers will continue toincrease in order to address the customers’ exponentially increasing need for mobile data. This is an illustrationof the challenge faced by the mobile operators for operating such a complex network with low OperationalExpenditures (OPEX) in order to stay competitive. This thesis is about leveraging the Self-Organizing Network(SON) concept to reduce this complexity by automating repetitive or complex tasks. We specifically proposeautomatic optimization algorithms for scenarios related to network densification using either small cells orActive Antenna Systems (AASs) used for Vertical Sectorization (VeSn), Virtual Sectorization (ViSn) and multilevelbeamforming. Problems such as load balancing with limited-capacity backhaul and interference coordination eitherin time-domain (eICIC) or in frequency-domain are tackled. We also propose optimal activation algorithms forVeSn and ViSn when their activation is not always beneficial. We make use of results from stochastic approximationand convex optimization for the mathematical formulation of the problems and their solutions. We also proposea generic methodology for the coordination of multiple SON algorithms running in parallel using results fromconcave game theory and Linear Matrix Inequality (LMI)-constrained optimization
Yen, Jou-An, and 顏柔安. "Projection Methods for Constrained Convex Optimization." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/73418791401906646487.
Повний текст джерела國立中山大學
應用數學系研究所
102
In this paper, we study the problem of finding a common minimizer of a finite family of constrained minimization problems. We convert this problem into an equivalent problem of finding a common fixed point of a finite family of nonexpansive mappings. Our methods are basically projection methods. We use three kinds of projection methods which are cyclic, parallel and successive, respectively. We prove that the sequence generated by each of these three projection methods weakly converges to an optimal solution of the problem.
Espinoza, Francisco. "A New Interpolation Approach for Linearly Constrained Convex Optimization." Thesis, 2012. http://hdl.handle.net/10754/244891.
Повний текст джерела"Approximation algorithms for Lp-ball and quadratically constrained polynomial optimization problems." 2013. http://library.cuhk.edu.hk/record=b6115682.
Повний текст джерелаIn this thesis, we present polynomial time approximation algorithms for solving various homogeneous polynomial optimization problems and their multilinear relaxations. Specifically, for the problems with Lp ball constraint, where P∈ [2 ,∞], by reducing them to that of determining the Lq-diameter of certain convex body, we show that they can be approximated to within a factor of [with formula] in deterministic polynomial time, where q = p=(p - 1) is the conjugate of p, n is the number of variables, and d is the degree of the polynomial. We further show that with the help of randomization, the approximation guarantee can be improved to [with formula], which is independent of p and is currently the best for the aforementioned problems. Moreover, we extend the argument of deterministic algorithm mentioned above to solve the quadratically constrained polynomial optimization problems. In particular, for any intersection of ellipsoids K, we can, in polynomial time, construct a random polytope P, which satisfies [with formula]. Then, by reducing the problem to that of evaluating the maximum polytopal norm [with formula] induced by P, over certain convex body, we can approximate the quadratically constrained problem within a factor of [with formula] in polynomial time. Our results unify and generalize those in the literature, which focus either on the quadratic case or the case where [with formula]. We believe that the wide array of tools used in this thesis will have further applications in the study of polynomial optimization problems.
Detailed summary in vernacular field only.
Hou, Ke.
On title page "p" is subscript.
Thesis (Ph.D.) Chinese University of Hong Kong, 2013.
Includes bibliographical references (leaves 106-111).
Abstracts also in Chinese.
"A value estimation approach to Iri-Imai's method for constrained convex optimization." 2002. http://library.cuhk.edu.hk/record=b5891236.
Повний текст джерелаThesis (M.Phil.)--Chinese University of Hong Kong, 2002.
Includes bibliographical references (leaves 93-95).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 2 --- Background --- p.4
Chapter 3 --- Review of Iri-Imai Algorithm for Convex Programming Prob- lems --- p.10
Chapter 3.1 --- Iri-Imai Algorithm for Convex Programming --- p.11
Chapter 3.2 --- Numerical Results --- p.14
Chapter 3.2.1 --- Linear Programming Problems --- p.15
Chapter 3.2.2 --- Convex Quadratic Programming Problems with Linear Inequality Constraints --- p.17
Chapter 3.2.3 --- Convex Quadratic Programming Problems with Con- vex Quadratic Inequality Constraints --- p.18
Chapter 3.2.4 --- Summary of Numerical Results --- p.21
Chapter 3.3 --- Chapter Summary --- p.22
Chapter 4 --- Value Estimation Approach to Iri-Imai Method for Con- strained Optimization --- p.23
Chapter 4.1 --- Value Estimation Function Method --- p.24
Chapter 4.1.1 --- Formulation and Properties --- p.24
Chapter 4.1.2 --- Value Estimation Approach to Iri-Imai Method --- p.33
Chapter 4.2 --- "A New Smooth Multiplicative Barrier Function Φθ+,u" --- p.35
Chapter 4.2.1 --- Formulation and Properties --- p.35
Chapter 4.2.2 --- "Value Estimation Approach to Iri-Imai Method by Us- ing Φθ+,u" --- p.41
Chapter 4.3 --- Convergence Analysis --- p.43
Chapter 4.4 --- Numerical Results --- p.46
Chapter 4.4.1 --- Numerical Results Based on Algorithm 4.1 --- p.46
Chapter 4.4.2 --- Numerical Results Based on Algorithm 4.2 --- p.50
Chapter 4.4.3 --- Summary of Numerical Results --- p.59
Chapter 4.5 --- Chapter Summary --- p.60
Chapter 5 --- Extension of Value Estimation Approach to Iri-Imai Method for More General Constrained Optimization --- p.61
Chapter 5.1 --- Extension of Iri-Imai Algorithm 3.1 for More General Con- strained Optimization --- p.62
Chapter 5.1.1 --- Formulation and Properties --- p.62
Chapter 5.1.2 --- Extension of Iri-Imai Algorithm 3.1 --- p.63
Chapter 5.2 --- Extension of Value Estimation Approach to Iri-Imai Algo- rithm 4.1 for More General Constrained Optimization --- p.64
Chapter 5.2.1 --- Formulation and Properties --- p.64
Chapter 5.2.2 --- Value Estimation Approach to Iri-Imai Method --- p.67
Chapter 5.3 --- Extension of Value Estimation Approach to Iri-Imai Algo- rithm 4.2 for More General Constrained Optimization --- p.69
Chapter 5.3.1 --- Formulation and Properties --- p.69
Chapter 5.3.2 --- Value Estimation Approach to Iri-Imai Method --- p.71
Chapter 5.4 --- Numerical Results --- p.72
Chapter 5.4.1 --- Numerical Results Based on Algorithm 5.1 --- p.73
Chapter 5.4.2 --- Numerical Results Based on Algorithm 5.2 --- p.76
Chapter 5.4.3 --- Numerical Results Based on Algorithm 5.3 --- p.78
Chapter 5.4.4 --- Summary of Numerical Results --- p.86
Chapter 5.5 --- Chapter Summary --- p.87
Chapter 6 --- Conclusion --- p.88
Bibliography --- p.93
Chapter A --- Search Directions --- p.96
Chapter A.1 --- Newton's Method --- p.97
Chapter A.1.1 --- Golden Section Method --- p.99
Chapter A.2 --- Gradients and Hessian Matrices --- p.100
Chapter A.2.1 --- Gradient of Φθ(x) --- p.100
Chapter A.2.2 --- Hessian Matrix of Φθ(x) --- p.101
Chapter A.2.3 --- Gradient of Φθ(x) --- p.101
Chapter A.2.4 --- Hessian Matrix of φθ (x) --- p.102
Chapter A.2.5 --- Gradient and Hessian Matrix of Φθ(x) in Terms of ∇xφθ (x) and∇2xxφθ (x) --- p.102
Chapter A.2.6 --- "Gradient of φθ+,u(x)" --- p.102
Chapter A.2.7 --- "Hessian Matrix of φθ+,u(x)" --- p.103
Chapter A.2.8 --- "Gradient and Hessian Matrix of Φθ+,u(x) in Terms of ∇xφθ+,u(x)and ∇2xxφθ+,u(x)" --- p.103
Chapter A.3 --- Newton's Directions --- p.103
Chapter A.3.1 --- Newton Direction of Φθ (x) in Terms of ∇xφθ (x) and ∇2xxφθ(x) --- p.104
Chapter A.3.2 --- "Newton Direction of Φθ+,u(x) in Terms of ∇xφθ+,u(x) and ∇2xxφθ,u(x)" --- p.104
Chapter A.4 --- Feasible Descent Directions for the Minimization Problems (Pθ) and (Pθ+) --- p.105
Chapter A.4.1 --- Feasible Descent Direction for the Minimization Prob- lems (Pθ) --- p.105
Chapter A.4.2 --- Feasible Descent Direction for the Minimization Prob- lems (Pθ+) --- p.107
Chapter B --- Randomly Generated Test Problems for Positive Definite Quadratic Programming --- p.109
Chapter B.l --- Convex Quadratic Programming Problems with Linear Con- straints --- p.110
Chapter B.l.1 --- General Description of Test Problems --- p.110
Chapter B.l.2 --- The Objective Function --- p.112
Chapter B.l.3 --- The Linear Constraints --- p.113
Chapter B.2 --- Convex Quadratic Programming Problems with Quadratic In- equality Constraints --- p.116
Chapter B.2.1 --- The Quadratic Constraints --- p.117
Donnelly, Catherine. "Convex duality in constrained mean-variance portfolio optimization under a regime-switching model." Thesis, 2008. http://hdl.handle.net/10012/4004.
Повний текст джерелаLi, Zhuo. "Distributed model predictive control based consensus of general linear multi-agent systems with input constraints." Thesis, 2020. http://hdl.handle.net/1828/11683.
Повний текст джерелаGraduate
2021-03-31
Alsaif, Muhanned. "New Algorithms to Solve the Positioning Problem of Outdoor Localization Using Constrained and Unconstrained Optimization Techniques." Thesis, 2021. http://hdl.handle.net/10754/670250.
Повний текст джерелаHung, Hsi, and 洪勗. "UFO: Unified Convex Optimization Algorithms for Fixed-Outline Floorplanning with Pre-placed Constraint." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/38042143644804472665.
Повний текст джерела國立成功大學
電機工程學系碩博士班
97
Fixed-outline floorplanning has attracted more attention for the real requirement in current IC design flow. In addition, there may exist several pre-placed modules in the specified region. In order to get a feasible foorplan, a floorplanner should have the ability to consider these constraints, which makes foorplanning a more difficult problem. In this thesis, we propose a it first work to consider pre-placed modules in a fixed-outline floorplan. Two-stages unified convex optimization methods, called UFO, are used in a global distribution and a local legalization stages, respectively. In the first stage, a pull-push model is used to distribute modules over a fixed outline under the wirelength consideration. In the second stage, the geometrical relations between modules are extracted by applying the Delaunay triangulation method on the result of the first stage. Then, a quadratic function as well as the constraint graphs are used to determine the locations and shapes of modules so that no module overlaps and all modules are in the outline. Experimental results have demonstrated that UFO clearly outperforms the results reported in the literature on the GSRC benchmarks.
Deng, Huizhong. "Shape Clustering and Spatial-temporal Constraint for Non-rigid Structure from Motion." Master's thesis, 2017. http://hdl.handle.net/1885/113634.
Повний текст джерелаDemir, Nazlı. "Decentralized probabilistic density control of swarm of autonomous agents with conflict avoidance constraints." Thesis, 2014. http://hdl.handle.net/2152/26210.
Повний текст джерелаtext
Cai, Pei Li. "Spectrum Sharing in Cognitive Radio Systems Under Outage Probablility Constraint." 2009. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7463.
Повний текст джерелаHeinrich, André. "Fenchel duality-based algorithms for convex optimization problems with applications in machine learning and image restoration." Doctoral thesis, 2012. https://monarch.qucosa.de/id/qucosa%3A19869.
Повний текст джерелаLorenz, Nicole. "Application of the Duality Theory: New Possibilities within the Theory of Risk Measures, Portfolio Optimization and Machine Learning." Doctoral thesis, 2011. https://monarch.qucosa.de/id/qucosa%3A19760.
Повний текст джерелаAkhil, P. T. "Topics in Network Utility Maximization : Interior Point and Finite-step Methods." Thesis, 2017. http://hdl.handle.net/2005/3268.
Повний текст джерелаGeorgiou, Konstantinos. "Integrality Gaps for Strong Linear Programming and Semidefinite Programming Relaxations." Thesis, 2010. http://hdl.handle.net/1807/26271.
Повний текст джерела