Sökning: "Optimization methods."
Visar resultat 1 - 5 av 1245 avhandlingar innehållade orden Optimization methods..
1. Νοήμονες υπολογιστικές μέθοδοι εμπνευσμένες από τον φυσικό κόσμο για την βελτιστοποίηση συστημάτων : αλγόριθμος βελτιστοποίησης εμπνευσμένης από τον ηχοεντοπισμό
Sammanfattning : Η εποχή που διανύουμε φέρνει στο προσκήνιο την Τεχνητή Νοημοσύνη, καθότι διάφοροι κλάδοι ενσωματώνουν εντυπωσιακές εφαρμογές της. Πολλές αναφορές γίνονται στην 4η Βιομηχανική Επανάσταση, η οποία έχει πλέον την Τεχνητή Νοημοσύνη ως κύριο πυλώνα της. LÄS MER
2. Production scheduling and shipment planning at oil refineries : optimization based methods
Sammanfattning : In the oil refinery industry, companies need to have a high utilization of production, storage, and transportation resources to be competitive. This can only be achieved by proper planning The purpose of this thesis is to contribute to the development of optimization models and solution methods that support the scheduling and planning at refinery companies. LÄS MER
3. Simultaneous Topology and Material Optimization of Composite Structures under Uncertainty
Sammanfattning : Composite materials are known to have superior stiffness and strength properties per unit weight compared to metallic materials. These properties and the ability to tailor the mechanical properties of composites is the main motivation for choosing composite materials for structural components. LÄS MER
4. Mean-Variance Portfolio Optimization : Eigendecomposition-Based Methods
Sammanfattning : Modern portfolio theory is about determining how to distribute capital among available securities such that, for a given level of risk, the expected return is maximized, or for a given level of return, the associated risk is minimized. In the pioneering work of Markowitz in 1952, variance was used as a measure of risk, which gave rise to the wellknown mean-variance portfolio optimization model. 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