Sökning: "Optimal order methods"
Visar resultat 1 - 5 av 399 avhandlingar innehållade orden Optimal order methods.
1. Optimal thinning : a theoretical investigation on individual-tree level
Sammanfattning : Paper I: In paper I, we asked how a tree should optimally allocate its resources to maximize its fitness. We let a subject tree grow in an environment shaded by nearby competing trees. The competitors were assumed to have reached maturity and had stopped growing, thus creating a static light environment for the subject tree to grow in. LÄS MER
2. Convergence Analysis and Improvements for Projection Algorithms and Splitting Methods
Sammanfattning : 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. LÄS MER
3. On Numerical Solution Methods for Block-Structured Discrete Systems
Sammanfattning : The development, analysis, and implementation of efficient methods to solve algebraic systems of equations are main research directions in the field of numerical simulation and are the focus of this thesis. Due to their lesser demands for computer resources, iterative solution methods are the choice to make, when very large scale simulations have to be performed. LÄS MER
4. Computational Diffusion MRI: Optimal Gradient Encoding Schemes
Sammanfattning : Diffusion-weighted magnetic resonance imaging (dMRI) is a non-invasivestructural imaging technique that provides information about tissue microstructures.Quantitative measures derived from dMRI reflect pathologicaland developmental changes in living tissues such as human brain. LÄS MER
5. Scalable Optimization Methods for Machine Learning : Acceleration, Adaptivity and Structured Non-Convexity
Sammanfattning : 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. LÄS MER