Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 315 for “"Optimization Algorithms"”.
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Adaptive global optimization algorithms
Global optimization is concerned with finding the minimum value of a function where many local minima may exist. The development of a global optimization algorithm may involve using information about the target function (e.g., differentiability) and functions based on statistical models to better …
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Optimization Algorithms for Protein Bioinformatics
… how to exploit these results to design powerful algorithms.
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Logic synthesis and optimization algorithms
… logic networks), are studied, and effective algorithms are developed for the use as automatic logic synthesis tools.
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Distributed Optimization Algorithms for Networked Systems
<p>Distributed optimization methods allow us to decompose an optimization problem</p><p>into smaller, more manageable subproblems that are solved in parallel. For this</p><p>reason, they are widely used to solve large-scale problems arising in areas as diverse</p><p>as wireless communications, …
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Optimization algorithms for loading military diesel generators
… need, this study compares the use of several optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), cuckoo search (CS), first fit decreasing (FFD) bin packing, and an exhaustive search (ES) method. It is found that at large enough search spaces, the …
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Hedging optimization algorithms for deregulated electricity markets
… of deregulated electricity markets and research algorithms to utilize these models to hedge risk. First, we consider the issue of calibrating these models to historical data. Once the models are calibrated sufficiently, we discuss two major frameworks for hedging risk optimally. We begin by first …
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Improving binary optimization algorithms using genuine uniform initialization
Population-based metaheuristic algorithms play a crucial role in solving complex optimization problems. The effectiveness of these algorithms is significantly influenced by the initial population of candidate solutions. This thesis investigates the critical aspect of initialization in …
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Hybrid classical-quantum optimization algorithms in electromagnetic applications
… paradigm for tackling large-scale combinatorial optimization problems. This dissertation develops a unified Ising-based framework for electromagnetic design, with a particular focus on reconfigurable intelligent surfaces (RISs) and antenna array synthesis. A broad class of electromagnetic …
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Optimization algorithms in boiling water reactor lattice design
… nuclear reactor. The current popular methods for optimization are Simulated Annealing and the Genetic Algorithm; this paper explores the potential for a new method called Greedy Exhaustive Dual Binary Swaps (GEDBS). The mandatory trade-off in computation is accuracy for speed; GEDBS is an …
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Designing policy optimization algorithms for multi-agent reinforcement learning
… settings and to build reliable and efficient algorithms that exploit and/or respect the structure. First, we observe that many data-driven algorithms in RL such as the gradient temporal difference learning and actor-critic algorithms essentially solve a bi-level optimization problem by …
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Reconfigurable Autonomous Surface Vehicles : perception and trajectory optimization algorithms
… ASVs in urban waterways. LiDAR-based perception algorithms are presented to enable robust and precise obstacle avoidance and object pose estimation on the water. Additionally, operating ASVs in well-networked urban waterways creates many potential use cases for ASVs to serve as re-configurable …
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Resource optimization algorithms for an automated coordinated CubeSat constellation
We present and analyze the performance of two algorithms that plan and coordinate activities for a resource-constrained Earth-observing CubeSat constellation. The first algorithm is the Resource-Aware SmallSat Planner (RASP), which performs low-level planning of observation and communication …
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Stochastic optimization algorithms for adaptive modulation in software defined radio
… systems. In addition, in closed form optimization, real time adaptation is not possible. Systems designed with deterministic state optimization are developed offline for a certain set of parameters and hardwired into mobile devices. In this thesis we present stochastic learning …
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Control Theoretic Methods In Analysis And Design Of Optimization Algorithms
… control theory to analyze and design iterative optimization algorithms. This new perspective provides many insights and new directions of research. In particular, we can study robustness to uncertainties, provide nonconservative performance guarantees, and envision principled algorithm design. …
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TOWARDS EFFICIENT LARGE-SCALE BI-LEVEL OPTIMIZATION, ALGORITHMS AND APPLICATIONS
Bi-level optimization is a mathematical framework with a long history of research, dealing with hierarchical optimization problems where one problem is nested within the other. Recently, with the rise of machine learning, bi-level optimization has regained attention as a theoretical framework …
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Geometric optimization algorithms for linear regression on fixed-rank matrices
… demand for efficient machine learning algorithms that can cope with large-scale problems, characterized by a large number of samples and a large number of variables. The research reported in the present thesis is devoted to the design of efficient machine learning algorithms for …
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Optimization Algorithms for Structured Machine Learning and Image Processing Problems
Optimization algorithms are often the solution engine for machine learning and image processing techniques, but they can also become the bottleneck in applying these techniques if they are unable to cope with the size of the data. With the rapid advancement of modern technology, data of …
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Advances in robust and adaptive optimization : algorithms, software, and insights
Optimization in the presence of uncertainty is at the heart of operations research. There are many approaches to modeling the nature of this uncertainty, but this thesis focuses on developing new algorithms, software, and insights for an approach that has risen in popularity over the last 15 years: …
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