Sökning: "low-rank approximation"
Visar resultat 1 - 5 av 11 avhandlingar innehållade orden low-rank approximation.
Sammanfattning : 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. LÄS MER
Sammanfattning : We study numerical methods for time-dependent partial differential equations describing wave propagation, primarily applied to problems in quantum dynamics governed by the time-dependent Schrödinger equation (TDSE). We consider both methods for spatial approximation and for time stepping. LÄS MER
Sammanfattning : In many fields of science, engineering, and economics large amounts of data are stored and there is a need to analyze these data in order to extract information for various purposes. Data mining is a general concept involving different tools for performing this kind of analysis. LÄS MER
4. Machine Learning Methods Using Class-specific Subspace Kernel Representations for Large-Scale Applications
Sammanfattning : Kernel techniques became popular due to and along with the rising success of Support Vector Machines (SVM). During the last two decades, the kernel idea itself has been extracted from SVM and is now widely studied as an independent subject. LÄS MER
Sammanfattning : In this thesis we present new worst case computational bounds on algorithms for some of the most well-known NP-complete and #P-complete problems and their optimization variants. We consider graph problems like Longest Path, Maximum Cut, Number of Perfect Matchings, Chromatic and Domatic Number, as well as Maximum k-Satisfiability and Set Cover. LÄS MER