Search for dissertations about: "proximal optimization"
Showing result 1 - 5 of 16 swedish dissertations containing the words proximal optimization.
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1. Inverse problems in signal processing : Functional optimization, parameter estimation and machine learning
Abstract : Inverse problems arise in any scientific endeavor. Indeed, it is seldom the case that our senses or basic instruments, i.e., the data, provide the answer we seek. READ MORE
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2. Scalable Optimization Methods for Machine Learning : Acceleration, Adaptivity and Structured Non-Convexity
Abstract : This thesis aims at developing efficient optimization algorithms for solving large-scale machine learning problems. To cope with the increasing scale and complexity of such models, we focus on first-order and stochastic methods in which updates are carried out using only (noisy) information about function values and (sub)gradients. READ MORE
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3. Asynchronous First-Order Algorithms for Large-Scale Optimization : Analysis and Implementation
Abstract : Developments in communication and data storage technologies have made large-scale data collection more accessible than ever. The transformation of this data into insight or decisions typically involves solving numerical optimization problems. READ MORE
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4. Large-Scale Optimization With Machine Learning Applications
Abstract : This thesis aims at developing efficient algorithms for solving some fundamental engineering problems in data science and machine learning. We investigate a variety of acceleration techniques for improving the convergence times of optimization algorithms. READ MORE
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5. Convergence Analysis and Improvements for Projection Algorithms and Splitting Methods
Abstract : Non-smooth convex optimization problems occur in all fields of engineering. A common approach to solving this class of problems is proximal algorithms, or splitting methods. These first-order optimization algorithms are often simple, well suited to solve large-scale problems and have a low computational cost per iteration. READ MORE