Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 22 for “"quadratic optimization"”.
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Inexact interior point methods for constrained convex quadratic optimization problems
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms
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Relaxation and exact algorithms for solving mixed integer-quadratic optimization problems
… various algorithms for solving mixed integer-quadratic problems. These problems exhibit exponential complexity resulting from the presence of integer variables. Traditional approaches that apply in pure integer programming are not very helpful, since the existence of continuous variables in …
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On Efficient Solution Methods for Mixed-Integer Nonlinear and Mixed-Integer Quadratic Optimization Problems
… methods for convex mixed-integer nonlinear optimization problems (MINLP). As one main result, we propose a new algorithm guaranteeing global optimality for convex MINLPs under standard assumptions. The new algorithm called MIQP-supported outer approximation (MIQPSOA) incorporates the …
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Greed, hedging, and acceleration in convex optimization
… second main message is a universality result for quadratic optimization. We show that, roughly speaking, "most" Krylov-subspace algorithms are asymptotically optimal (in the worst-case) and "most" quadratic functions are asymptotically worst-case functions (for all algorithms). From an algorithmic …
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Discrete Approximations, Relaxations, and Applications in Quadratically Constrained Quadratic Programming
… on theory and applications for Mixed Integer Quadratically Constrained Quadratic Programs (MIQCQP). We introduce new mixed integer programming (MIP)-based relaxation and approximation schemes for general Quadratically Constrained Quadratic Programs (QCQP's), and also study practical …
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Generalized Matrix-fractional Functions and Their Applications
… variational properties of linear constrained quadratic optimization problems, generalized Ky Fan norms, variational Gram functions (VGF), the Aitken's theorem and Gauss-Markov theorem in statistical estimation, and many topics in machine learning such as K-means clustering, support vector …
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Autonomous altitude estimation of a miniature helicopter using a single onboard camera.
… distribution using the MAP estimate by solving a quadratic optimization problem with L1 regularity constraints. The method is evaluated in a laboratory setting with a real helicopter and is found to provide promising results with sufficiently fast turnaround time.
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Robust optimization
We propose new methodologies in robust optimization that promise greater tractability, both theoretically and practically than the classical robust framework. We cover a broad range of mathematical optimization problems, including linear optimization (LP), quadratic constrained quadratic …
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Perception methods for continuous humanoid locomotion over uneven terrain
… online footstep re-planning using mixed-integer quadratic optimization. The approach is implemented within a novel software framework called Director, and results are validated on hardware using the Atlas humanoid robot with autonomous laboratory experiments and semi-autonomous field experiments …
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Convexification and Global Optimization of Problems Involving the Euclidean Norm
The field of deterministic global optimization has advanced significantly over the last several decades, enabled by the development of new algorithmic techniques and improved computer hardware, and is experiencing a surge of interest. However, global optimization methods for general nonlinear …
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SVM-based Strategies as applied to Electromagnetics
… method compared to more traditional optimization techniques solves the arising quadratic optimization problem with constraints in a simple and reliable way leveraging on the statistical learning theory which enables the design of optimal classifiers with a solid theoretical framework. …
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LP-based subgradient algorithm for joint pricing and inventory control problems
… and use its dual variables in order to solve a quadratic optimization problem that optimizes the revenue part and generates a new pricing policy. We illustrate computationally that this algorithm obtains the optimal production and pricing policy over the finite time horizon efficiently. The …
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Flexible Scheduling Methods and Tools for Real-Time Control Systems
… is obtained by the solution of a constrained quadratic optimization problem. A termination criterion is derived that, unlike traditional MPC, takes the effects of computational delay into account in the optimization. A scheduling scheme is also described, where the MPC cost functions being …
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Advanced modeling and computational methods for distribution system state estimation
… models for distribution systems and convex and quadratic optimization methods. Therefore, semidefinite and quadratic programming methods, enabled by alternative power flow models, are leveraged to provide accurate solutions that are robust to various measurement types yet computationally …
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Decomposition methods for large scale stochastic and robust optimization problems
… use on broad families of stochastic and robust optimization problems in order to yield tractable approaches for large-scale real world application. We introduce a new type of a Markov decision problem named the Generalized Rest less Bandits Problem that encompasses a broad generalization of the …
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New results on some quadratic programming problems
… algorithms for several special classes of quadratic programming problems. The problems we study can be classifiedinto two categories. The first group contains two optimization problems with binary constraints. To solve these problems, we first explore some intrinsic relation between binary …
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New Theory and Algorithms for Convex Optimization with Non-Standard Structures
Optimization models and algorithms have long played central and indispensable roles in the advancement of science and engineering. In recent years, first-order methods have played important roles in tackling applications arising in machine learning and data science, due to their simplicity, …
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Adjoint-accelerated Bayesian inference in thermoacoustics
… this framework, parameter inference reduces to a quadratic optimization problem, which we solve using gradient-based optimization, with gradients calculated using adjoint methods. We then estimate the posterior uncertainty using Laplace's method, which we evaluate using first and second order …
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Advances in Computer-Assisted Design and Analysis of First-Order Optimization Methods and Related Problems
First-order methods are optimization algorithms that can be described and analyzed using the values and gradients of the functions to be minimized. These methods have become the main workhorses for modern large-scale optimization and machine learning due to their low iteration costs, minimal memory …
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Solution of Constrained Clustering Problems through Homotopy Tracking
… machine learning methods are dependent on active optimization research to improve the set of methods available for the efficient and effective extraction of information from large datasets. This, in turn, requires an intense and rigorous study of optimization methods and their possible …
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