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 10 of 10 for “"first-order optimization"”.
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Dynamical systems view of acceleration in first order optimization
Gradient based optimization algorithms are among the most fundamental algorithms in optimization and machine learning, yet they suffer from slow convergence. Consequently, accelerating gradient based methods have become an important recent topic of study. In this thesis, we focus on explaining and …
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Accelerated first-order optimization methods using inertia and error bounds
Optimization is an important discipline of applied mathematics with far-reaching applications. Optimization algorithms often form the backbone of practical systems in machine learning, image processing, signal processing, computer vision, data analysis, and statistics. In an age of massive data …
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Advances in Computer-Assisted Design and Analysis of First-Order Optimization Methods and Related Problems
First-order methods are optimization algorithms that can be described and analyzed using the values and gradients of the functions to be minimized. These methods have become the main workhorses for modern large-scale optimization and machine learning due to their low iteration costs, minimal memory …
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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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Downhole vibration sensing by vibration energy harvesting
… device for use in a downhole environment. First order models of the necessary components for a generic vibration energy harvester are presented and used to predict the most sensitive parameters for the design of energy harvesting systems. A subset of the design tools created in MATLAB and …
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Investigating a Second-Order Optimization Strategy for Neural Networks
… of the conjugate gradient method CG for the optimization of artificial neural networks (NNs) and compares this method with common first-order optimization methods, especially the stochastic gradient descent (SGD). The presented research results show that CG can effectively optimize both small …
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A multidisciplinary algorithm for the 3-D design optimization of transonic axial compressor blades
… multidisciplinary algorithm for the CFD design optimization of turbomachinery blades is presented. It departs from existing techniques in that it uses a simple, previously-developed Bezier geometry representation (BLADE-3D) that can be easily manipulated to achieve true 3-D changes in blade …
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Beyond Gradients: Using Curvature Information for Deep Learning
This thesis investigates optimization and interpretability techniques for deep learning, extending beyond gradient-based methods to incorporate curvature information. We begin by addressing the limitations of first-order optimization methods like gradient descent, which can exhibit slow convergence …
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Combinatorial structures in online and convex optimization
… in algorithms across online and convex optimization, we consider three fundamental questions over combinatorial polytopes. First, we study the minimization of separable strictly convex functions over polyhedra. This problem is motivated by first-order optimization methods whose …
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Efficient learning of temporal dynamics with first-order methods
… efficient, by harnessing the power of first-order optimization methods. In particular, we provide solutions to the above challenges, by developing the following distinct, yet closely related algorithms: • First, we introduce an online learning framework for nonparametric maximum …