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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 24 for “"Unconstrained optimization"”.
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Scaling rank-one updating formula and its application in unconstrained optimization
This thesis deals with algorithms used to solve unconstrained optimization problems. We analyse the properties of a scaling symmetric rank one (SSRl) update, prove the convergence of the matrices generated by SSRl to the true Hessian matrix and show that algorithm SSRl possesses the quadratic …
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Scaling rank-one updating formula and its application in unconstrained optimization
This thesis deals with algorithms used to solve unconstrained optimization problems. We analyse the properties of a scaling symmetric rank one (SSRl) update, prove the convergence of the matrices generated by SSRl to the true Hessian matrix and show that algorithm SSRl possesses the quadratic …
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Development and application of multistep computational techniques for constrained and unconstrained mathematical functions
… the fact that these concepts can be extended to unconstrained optimization problems, and by penalty formulations, also to constrained optimization problems. This holds true not only for steepest descent methods but also for any other "improving direction." In the discussion of the application of …
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The Clarke Derivative and Set-Valued Mappings in the Numerical Optimization of Non-Smooth, Noisy Functions
… tool for the convergence analysis of numerical optimization methods. It is based on the concepts of the Clarke derivative and set-valued mappings. Our goal is to apply this tool to minimization problems with non-smooth and noisy objective functions. After deriving a necessary condition for …
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Qualitative and quantitative optimization of skylights : a comprehensive and inclusive analysis of skylight sizes for an office while providing enough daylight, avoiding glare and saving energy
… are proposed in this dissertation, encompassing unconstrained optimization, constrained optimization and monetary metrics. In the unconstrained optimization approach, the algorithmic platform has been developed to implement Parametric Analysis (PA) and Gradient Descent (GD) methods in order to …
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Optimization of sail design
… problems. The first problem deals with an unconstrained optimization of the lift. Then, to develop the second problem, we introduce an integrated form of Stratford's separation criterion as a limiting constraint. Throughout the work, we incorporate a vortex lattice analysis to determine the …
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Scalable second-order Riemannian optimization for K-means clustering
… for clustering is a worst-case NP-hard discrete optimization problem. Despite being NP-hard, the SDP relaxation of the discrete formulation is guaranteed to recover the true cluster whenever it is statistically solvable. In this thesis, we propose to solve the relaxed K-means problem as an …
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Large scale optimization for machine learning
… risk minimization problems, large scale optimization plays a key role in building a large scale machine learning system. However, scaling optimization algorithms like stochastic gradient descent (SGD) in a distributed system raises some issues like synchronization since they were not …
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A structured reduced sequential quadratic programming and its application to a shape design problem
… duct design problem using a particular optimization method. The design problem is formulated as an equality constrained optimization, called All at once method, so that the analysis problem is not solved until the optimal design is reached. Furthermore, the block structure in the …
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Iterative Reconstruction Algorithms for Polyenergetic X-ray Computerized Tomography
… and implement different solvers and nonlinear unconstrained optimization methods, such as a Newton-like method and an extension of the Levenberg-Marquardt-Fletcher algorithm. We explain how we can use the structure of the Radon matrix and the properties of FBP to make our method matrix-free and …
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Optimization of neural network feedback control systems using automatic differentiation
… input in both trajectory and feedback control optimization problems. The weights of the neural network that minimize a cost function are determined by an unconstrained optimization routine. By using automatic differentiation on the code that evaluates the cost function, the gradient of the cost …
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Algorithms above the noise floor
… arising in the data sciences. * In constrained optimization, we show that it is possible to optimize over a wide range of non-convex sets up to the statistical noise floor. * In unconstrained optimization, we prove that important convex problems already require approximation if we want to find a …
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Adaptive sampling trust-region methods for derivative-based and derivative-free simulation optimization problems
<p>We consider unconstrained optimization problems where only “stochastic” estimates of the objective function are observable as replicates from a Monte Carlo simulation oracle. In the first study we assume that the function gradients are directly observable through the Monte Carlo simulation. We …
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An Improved Genetic Algorithm for the Optimization of Composite Structures
… of mixed discrete-continuous variables. The optimization of these models is typically difficult due to their combinatorial nature and potential existence of multiple local minima in the search space. Genetic algorithms (GAs) are powerful tools for solving such problems. GAs do not require …
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An Optimal Control Approach to Implant Shape Design : Modeling, Analysis and Numerics
… is concerned with the efficient solution and optimization of realistic models. This includes recent material laws for different soft tissue types as well as complex geometries attained from medical image data. The implant shape design problem can be described as an optimal control problem with …
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Agile load transportation systems using aerial robots
… based on the Nelder-Mead algorithm which is an optimization technique used for nonlinear unconstrained optimization problems. This method is model free and it can be used for offline or online generation of the swing-free trajectories for the suspended load. Besides the swing-free maneuvers with …
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Optimum performance of solenoid injectors for direct injection of gaseous fuels in IC engines
… multivariable multiobjective constrained optimization procedure is developed to establish an optimal design. An optimized injection system could minimize the time delays and shape the profile of the injector needle motion in order to reduce the deviation between the expected and actual …
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Scaling Bayesian optimization for engineering design : lookahead approaches and multifidelity dimension reduction
… Accordingly, whether the task is formal optimization or just design space exploration, there is often a finite budget specifying the maximum number of evaluations of the objectives and constraints allowed. Bayesian optimization (BO) has become a popular global optimization technique for …
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Allocation strategy for production network designed to mitigate risk
… typically higher at either RMS or Site 1A/B, an unconstrained optimization model might suggest filling/finishing all product at whichever site has the lowest average cost. However, we assume that RMS should be able to ramp up to full capacity within 3 months of an adverse occurrence at Site 1A. …
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Optimization by Gaussian smoothing with application to geometric alignment
It is well-known that global optimization of a nonconvex function, in general, is computationally intractable. Nevertheless, many objective functions that we need to optimize may be nonconvex. In practice, when working with such a nonconvex function, a very natural heuristic is to employ a …
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