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 204 for “"Non-Convex"”.
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Hardy type inequalities for non-convex domains
… a study of certain Hardy-type inequalities for non-convex domains \(\Omega\) \(\subset\)R\(^n\). Please see the thesis for a fuller abstract.
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Global Non-Convex Optimization with Integer Variables
Non-convex optimization refers to the process of solving problems whose objective or constraints are non-convex. Historically, this type of problems have been very difficult to solve to global optimality, with traditional solvers often relying on approximate solutions. Bertsimas et al. [1] …
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Topics in non-convex optimization and learning
Non-convex optimization and learning play an important role in data science and machine learning, yet so far they still elude our understanding in many aspects. In this thesis, I study two important aspects of non-convex optimization and learning: Riemannian optimization and deep neural networks. …
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Polynomial Structure in Semidefinite Relaxations and Non-Convex Formulations
… tool used to approximate otherwise intractable non-convex problems, but tend to run into scalability issues in large-scale instances. The goal of this thesis is to explore the power of semidefinite relaxations and address the scalability issues, for special classes of problems with polynomial …
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Rapid detection of shallow penetration between non-convex polyhedra
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2001.
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Optimizing Non-Convex Objectives to Plan More Optimal Motion for Manipulators
Non-convex optimization is essential to tackle increasingly complex and practical problems in kinematic motion planning. Although introducing non-convexity often sacrifices guarantees of feasibility and optimality–making solutions more susceptible to local minima or failure to converge–many robotic …
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Dynamics of a continuum characterized by a non-convex energy function
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1993.
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Traversing Rugged Domains: Explorations in Non-convex Optimization Theory and Software
… theoretical and computational frameworks for nonlinear, nonconvex optimization problems in statistics, machine learning, and optimal control. Disciplined Geodesically Convex Programming (DGCP) extends convexity verification to Riemannian manifolds, enabling optimization on curved spaces with …
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Cooperative Game Theory and Non-convex Optimization Analysis of Spectrum Sharing
… interactions in the wireless medium can lead to non-convex problems which have been shown to be NP-hard. 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 …
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Dielectric elastomer composites: analytical and numerical non-convex homogenization methods and applications
… of Eshelby and Maxwell to the coupled and nonlinear realm of electroelastostatics, the above-outlined rigorous asymptotic solutions turn out to be essential in the development of corresponding homogenization solutions for finite deformations and finite electric fields. Indeed, it is shown …
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A non-convex framework for structured non-stationary covariance recovery theory and application
… a structured time-indexed covariance model for non-stationary time-series data by decomposing them into sparse spatial and temporally smooth components. Traditionally, time-indexed covariance models without structure require a large sample size to be estimable. While the covariances …
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Understanding and Overcoming Optimization Barriers in Non-convex and Non-smooth Machine Learning
At their core, our machine learning systems are trained by solving an optimization problem, where the goal is to minimize a predefined objective function by adjusting model parameters based on the data. Despite the wealth of structure and prior knowledge present in the data and feedback, our …
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A Numerical Investigation Of The Canonical Duality Method For Non-Convex Variational Problems
… theoretical and numerical investigation of the canonical duality theory, which has been recently proposed as an alternative to the classic and direct methods for non-convex variational problems. These non-convex variational problems arise in a wide range of scientific and engineering applications, …
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Applicability of deep learning approaches to non-convex optimization for trajectory-based policy search
… Second, three systems built for parallelized, non-convex optimization across trajectories with a shared neural network constraint are described and analyzed. Finally, techniques from deep learning known to improve convergence speed and quality in non-convex optimization are studied when applied …
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Three Problems Involving Compressible Flow with Large Bulk Viscosity and Non-Convex Equations of State
… flows of Navier-Stokes fluids. In each problem non-classical effects are considered. In the first two problems, we consider fluids which have bulk viscosities which are much larger than their shear viscosities. In the last problem, we examine steady supersonic flows of a …
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Out-of-equilibrium sampling as a protocol to unveil locally-smooth neural network configurations in non-convex optimization
L'abstract è presente nell'allegato / the abstract is in the attachment
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Contraction maps and applications to the analysis of iterative algorithms
… community, and especially machine learning, on non-convex problems, has made non-convex optimization one of the most important and challenging areas of our days. Despite of this increasing interest too little is known from a theoretical point of view. The main reason for this is that the …
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Learning and optimization in the face of data perturbations
… optimization problems: 1) the function is often non-convex, making optimization difficult; 2) the distribution is not known exactly, but may be perturbed adversarially or is otherwise obscured. Each issue is individually so challenging to warrant a substantial accompanying body of work addressing …
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On cutting planes for mixed-integer nonlinear programming
Mixed-integer nonlinear programming is a powerful technology that allows us to model and solve problems involving nonlinear functions, continuous, and discrete variables. The state-of-the-art solvers of mixed-integer nonlinear programs (MINLPs) use a combination of, among other techniques, branch- …
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