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 29 for “"Continuous Optimization"”.
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Solving Hybrid Boolean SAT by Continuous Optimization
… efforts for addressing critical limitations of a continuous-optimization-based framework for hybrid SAT solving, named FourierSAT. In FourierSAT, Boolean constraints are converted into polynomials via Walsh-Hadamard-Fourier Transform and constrained continuous optimizers are then applied to search …
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Advances in Hybrid Evolutionary Computation for Continuous Optimization
Evolutionary Algorithms (EAs) are a set of optimization techniques that have become highly popular in recent decades. One of the main reasons for this success is that they provide a general purpose mechanism for solving a wide range of problems. Several approaches have been proposed, each of them …
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Discrete-continuous optimization for robot perception via semidefinite relaxation
… semantic segmentation and robust pose graph optimization. Compared with other inference techniques, SDP relaxation have shown to provide accurate estimate with provable sub-optimality guarantees without relying on an initial guess for optimization. On the downside, general SDP solvers scale …
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Flows, Submodularity, Sparsity, and Beyond: Continuous Optimization Insights for Discrete Problems
… we build on connections between discrete and continuous optimization. In the first part of the thesis we propose faster second-order convex optimization algorithms for classical graph algorithmic problems. Our main contribution is to show that the runtime of interior point methods is closely …
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Shortest paths, Markov chains, matrix scaling and beyond : improved algorithms through the lens of continuous optimization
… connections between classic methods from convex optimization and the modern toolkit from the fast Laplacian solver literature, in order to make progress on a number of fundamental algorithmic problems: *-- We develop a faster algorithm for the unit capacity minimum cost flow problem, which …
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Dynamic Optimization in Hardware
… the relationship between well-known compiler optimization concepts and hardware-implemented optimizations. We define and explore two hardware-centric dynamic optimization paradigms: continuous optimization and discrete optimization. Continuous optimization optimizes instructions in the …
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No Free Lunch, Bayesian Inference, and Utility: A Decision-Theoretic Approach to Optimization
Existing approaches to continuous optimization are essentially mechanisms for deciding which locations should be sampled in order to obtain information about a target function's global optimum. These methods, while often effective in particular domains, generally base their decisions on heuristics …
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Optimal stacking sequence design of stiffened composite panels with cutouts
… has stimulated interest in the development of optimization procedures for the design of laminates. The design of composite structures against buckling presents two major challenges to the structural analyst and designer. First, the problem of laminate stacking sequence design is discrete in …
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Efficient Structure and Motion: Path Planning, Uncertainty and Sparsity
… state-of-the-art. The second paper presents a continuous optimization method for mobile robot path planning, designed to minimize the uncertainty of the geometry reconstructed from images taken by the robot. Similar concepts are applied in the third paper to the problem of sequential 3D …
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Neuro-Symbolic Learning for Bilevel Robot Planning
… in robotics domains is complicated by continuous state and action spaces, long horizons, and sparse feedback. One way to address these challenges is to perform bilevel planning, where decision-making is decomposed into reasoning about “what to do” (task planning) and “how to do it” …
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Cooperative Game Theory and Non-convex Optimization Analysis of Spectrum Sharing
… Techniques must be developed to tackle the optimization problems that arise from wireless network analysis. In this document we focus on analyzing the spectrum sharing problem from two perspectives: cooperative game theory and non-convex optimization. We develop a cooperative game theory …
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Integer optimization in data mining
While continuous optimization methods have been widely used in statistics and data mining over the last thirty years, integer optimization has had very limited impact in statistical computation. Thus, our objective is to develop a methodology utilizing state of the art integer optimization methods …
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Generalized partition crossover for the traveling salesman problem
… Problem (TSP) is a well-studied combinatorial optimization problem with a wide spectrum of applications and theoretical value. We have designed a new recombination operator known as Generalized Partition Crossover (GPX) for the TSP. GPX is unique among other recombination operators for the TSP …
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Non-branching hybrid transit network design under heterogeneous demand
… transit network on a square region. A nonlinear continuous optimization model is built upon this structure to minimize total transit system cost. One of this model’s main limitations is the uniform trip demand assumption, which constrains its applicability in real-world. Another pitfall is that …
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Water resources decision making using meta-heuristic optimization methods
… focus was to investigate meta-heuristic (global) optimization methods suitable for developing water resources decision support system (DSS), particularly to optimally design and operate groundwater storage and recovery projects. The effort included developing an integrated simulation-optimization …
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Nonlinear model reduction for uncertainty quantification in large-scale inverse problems : application to nonlinear convection-diffusion-reaction equation
… order approximation by solving a PDE-constrained continuous optimization problem that targets the output error between the reduced and full order models in order to determine the optimal sampling point at every greedy cycle. The resulting model reduction methodology is applied to a highly …
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Continuous Low-Thrust Trajectory Optimization: Techniques and Applications
Trajectory optimization is a powerful technique to analyze mission feasibility during mission design. High-thrust trajectory optimization problems are typically formulated as discrete optimization problems and are numerically well-behaved. Low-thrust systems, on the other hand, operate for …
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Large-Scale Algorithms for Machine Learning: Efficiency, Estimation Errors, and Beyond
Optimization algorithms stand as a cornerstone for machine learning and statistical inference. The advent of large-scale datasets introduces computational challenges, necessitating the pursuit of more efficient algorithms. Modern optimization techniques are usually tailored to particular machine …
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Learning Heuristics for Combinatorial Optimization Problems with Deep Neural Networks
Solving real-world combinatorial optimization problems with traditional operations research methods can be a costly and time-consuming endeavor, often requiring the development of completely new methods or significant modification of exist- ing techniques. This has led many organizations to forego …
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Optimal Control for Autonomous Motor Behavior
… the task as a cost function, and use numerical optimization to find action sequences that can accomplish the task. Using the theoretical framework of optimal control, we develop methods for generating autonomous motor behavior in high-dimensional domains of legged locomotion. We identify three …
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