Search for dissertations about: "Convex Relaxation"
Showing result 1 - 5 of 22 swedish dissertations containing the words Convex Relaxation.
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1. Computational Methods for Computer Vision : Minimal Solvers and Convex Relaxations
Abstract : Robust fitting of geometric models is a core problem in computer vision. The most common approach is to use a hypothesize-and-test framework, such as RANSAC. In these frameworks the model is estimated from as few measurements as possible, which minimizes the risk of selecting corrupted measurements. READ MORE
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2. Rank Reduction with Convex Constraints
Abstract : This thesis addresses problems which require low-rank solutions under convex constraints. In particular, the focus lies on model reduction of positive systems, as well as finite dimensional optimization problems that are convex, apart from a low-rank constraint. READ MORE
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3. Conditional Subgradient Methods and Ergodic Convergence in Nonsmooth Optimization
Abstract : The topic of the thesis is subgradient optimization methods in convex, nonsmooth optimization. These methods are frequently used, especially in the context of Lagrangean relaxation of large scale mathematical programs where they are remarkably often able to quickly identify near-optimal Lagrangean dual solutions. READ MORE
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4. Electromagnetic Modeling and Design of Medical Implants and Devices
Abstract : This thesis covers two topics in biomedical electromagnetics: pacemaker lead heating in magnetic resonance imaging (MRI) and optimization of sensorpositions in magnetic tracking.The electromagnetic part of pacemaker lead heating during MRI is a resonant phenomenon which is complicated by, among other factors, the wide range of length scales involved in the problem. READ MORE
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5. Convex Optimization for Assignment and Generalized Linear Regression Problems
Abstract : This thesis considers optimization techniques with applications in assignment and generalized linear regression problems. The first part of the thesis investigates the worst-case robust counterparts of combinatorial optimization problems with least squares (LS) cost functions, where the uncertainty lies on the linear transformation of the design variables. READ MORE
