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 256 for “"convex optimization"”.
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Robust boosting via convex optimization
In dieser Arbeit werden statistische Lernprobleme betrachtet. Lernmaschinen extrahieren Informationen aus einer gegebenen Menge von Trainingsmustern, so daß sie in der Lage sind, Eigenschaften von bisher ungesehenen Mustern - z.B. eine Klassenzugehörigkeit - vorherzusagen. Wir betrachten den Fall, …
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Constrained control using convex optimization
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1997.
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Subspace identification via convex optimization
In this thesis we consider convex optimization-based approaches to the classical problem of identifying a subspace from noisy measurements of a random process taking values in the subspace. We focus on the case where the measurement noise is component-wise independent, known as the factor analysis …
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Applications and limits of convex optimization
… becomes vastly more tractable when reduced to a convex program, yet few can be simplified this way. At the heart of this thesis are two hard problems with unexpected convex reformulations. The Paulsen problem, a longstanding open problem in operator theory, was recently resolved by Kwok et al …
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Convex optimization methods for model reduction
Model reduction and convex optimization are prevalent in science and engineering applications. In this thesis, convex optimization solution techniques to three different model reduction problems are studied.Parameterized reduced order modeling is important for rapid design and optimization of …
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Joint Equalization and Decoding via Convex Optimization
… for decoding and inference problems based on convex optimization methods. Th first part considers the joint detection and decoding problem for low-density parity-check (LDPC) codes on finite-state channels (FSCs). Hard-disk drives (or magnetic recording systems), where the required error rate …
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Combinatorial structures in online and convex optimization
… by bottlenecks in algorithms across online and convex optimization, we consider three fundamental questions over combinatorial polytopes. First, we study the minimization of separable strictly convex functions over polyhedra. This problem is motivated by first-order optimization methods whose …
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Global Non-Convex Optimization with Integer Variables
Non-convex optimization refers to the process of solving problems whose objective or constraints are non-convex. Historically, this type of problems have been very difficult to solve to global optimality, with traditional solvers often relying on approximate solutions. Bertsimas et al. [1] …
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Topics in non-convex optimization and learning
Non-convex optimization and learning play an important role in data science and machine learning, yet so far they still elude our understanding in many aspects. In this thesis, I study two important aspects of non-convex optimization and learning: Riemannian optimization and deep neural networks. …
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Greed, hedging, and acceleration in convex optimization
… motivated problem of minimizing a strongly convex, smooth function with first-order information. The first main message of the thesis is that, surprisingly, algorithms which are individually suboptimal can be combined to achieve accelerated convergence rates. This phenomenon can be intuively …
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Methods for convex optimization and statistical learning
… at the interface of first-order methods for convex optimization and problems in statistical machine learning. In the first part of this thesis, we present new results for the Frank-Wolfe method, with a particular focus on: (i) novel computational guarantees that apply for any step-size …
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Reducing spacecraft thruster plume contamination using convex optimization
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms
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Constrained H̳₂ design via convex optimization with applications
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 …
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Convex optimization methods for graphs and statistical modeling
… development of computational methods based on convex optimization, which are in turn useful in a broad array of problems in signal processing and machine learning. The specific contributions are as follows: -- We propose a convex optimization method for decomposing the sum of a sparse matrix …
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Integrated vehicle and mission design using convex optimization
Convex optimization is used to solve the simultaneous vehicle and mission design problem. The objective of this work is to develop convex optimization architectures that allow both the vehicle and mission to be designed together. They allow the problem to be solved very quickly while maintaining …
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Analog circuit optimization using evolutionary algorithms and convex optimization
… is a hybrid of a genetic algorithm and a convex optimization technique. We study the performance of the algorithm on artificially created benchmark problems. We show that the accuracy of posynomial models of MOS parameters is improved by a considerable amount by using the new algorithm. …
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Scalable, Efficient, and Fair Algorithms for Structured Convex Optimization Problems
… thesis, we present algorithms for fundamental optimization algorithms with theoretical guarantees on approximation quality and running time. We analyze the bit complexity and stability of efficient algorithms for problems including linear regression, $p$-norm regression, and linear programming …
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Computational analysis of real-time convex optimization for control systems
… software. Nevertheless, analysis of on-line optimization for control has received little attention to date. On-line software must pass rigorous standards in reliability, requiring that any embedded optimization algorithm possess predictable behavior and bounded run-time guarantees. This …
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Faster algorithms for matrix scaling and balancing via convex optimization
… these results, we develop a new second-order optimization framework that enables us to treat both problems in a unified and principled manner. This framework identifies a certain generalization of linear system solving which we can use to efficiently minimize a broad class of functions, which …
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