Search for dissertations about: "large-scale"
Showing result 1 - 5 of 2133 swedish dissertations containing the word large-scale.
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1. Asynchronous Algorithms for Large-Scale Optimization : Analysis and Implementation
Abstract : This thesis proposes and analyzes several first-order methods for convex optimization, designed for parallel implementation in shared and distributed memory architectures. The theoretical focus is on designing algorithms that can run asynchronously, allowing computing nodes to execute their tasks with stale information without jeopardizing convergence to the optimal solution. READ MORE
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2. Inducing large-scale diffusion of innovation : An integrated actor- and system-level approach
Abstract : In order for the innovation process to be successful, not only do innovations need to be developed and reached the market, but, once they are available for users, they have to spread on a large scale. In the innovation literature, a complete explanation is lacking of why some innovations reach a phase of large-scale diffusion faster than others, including both actor- and system-level components. 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. Tailoring Gaussian processes and large-scale optimisation
Abstract : This thesis is centred around Gaussian processes and large-scale optimisation, where the main contributions are presented in the included papers.Provided access to linear constraints (e.g. equilibrium conditions), we propose a constructive procedure to design the covariance function in a Gaussian process. READ MORE
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5. Accelerating Convergence of Large-scale Optimization Algorithms
Abstract : Several recent engineering applications in multi-agent systems, communication networks, and machine learning deal with decision problems that can be formulated as optimization problems. For many of these problems, new constraints limit the usefulness of traditional optimization algorithms. READ MORE