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 45 for “"Large Scale Optimization"”.
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Evolutionary Algorithm For Large-Scale Optimization
… processes, a decision is made by solving an optimization problem. However, the performance of an optimization algorithm deteriorates significantly with the size of the problems, more complex relationships among the variables, and non-standard properties of the fitness function. To deal with …
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Large scale optimization for machine learning
… to robotics. In entering the era of big data, large scale machine learning tools become increasingly important in training a big model on big data. Since machine learning problems are fundamentally empirical risk minimization problems, large scale optimization plays a key role in building a …
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Large-Scale Optimization Methods: Theory and Applications
Large-scale optimization problems appear quite frequently in data science and machine learning applications. In this thesis, we show the efficiency of coordinate descent (CD) and mirror descent (MD) methods in solving large-scale optimization problems. First, we investigate the convergence rate of …
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Large-scale optimization Methods for data-science applications
… this thesis, we present several contributions of large scale optimization methods with the applications in data science and machine learning. In the first part, we present new computational methods and associated computational guarantees for solving convex optimization problems using first-order …
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Large-scale optimization in online-retail inventory management
… satisfying demand and capacity constraints. The large scale of the problem is due to the size of the fulfillment center network and the number of items. We propose a large-scale solution scheme that aggregates the items, solves a column-based reformulation of the aggregated problem using column …
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Decomposition techniques for large-scale optimization in the supply chain
… center components of the supply chain. Initial optimization results are obtained for each of these models. Additionally, an integrated model including a single plant, multiple consolidation transport vehicles, and a single distribution center is formulated and initial results are obtained. All …
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The edge of large-scale optimization in transportation and machine learning
This thesis focuses on impactful applications of large-scale optimization in transportation and machine learning. Using both theory and computational experiments, we introduce novel optimization algorithms to overcome the tractability issues that arise in real world applications. We work towards …
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Large-scale Optimization for Robust Multi-Class Prediction and Resource Allocation
In this thesis we develop optimization-based methods to deal with uncertainty arising from data, first in the context of robust multi-class prediction and second for prescriptive analytics for medical resource allocation. In the first part, we make progress on training robust multi-class …
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Large-Scale Optimization using Reinforcement Learning, Dynamic Programming, and Column Generation
One of the most enduring challenges in large-scale optimization is determining how to push the boundaries of scalability without compromising on performance or rigor. For decades, the exponential advances in computational power offered a straightforward solution: bigger problems could simply be …
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Large-scale optimization for green logistics and stochastic resource allocation for food security
… problems in food supply chain systems utilizing large-scale optimization, stochastic resource allocation, and data-analytics methodologies. We focused on three main research questions: 1) How can retailers build green, efficient last-mile logistics system when the objective is to maximize their …
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Scaling rank-one updating formula and its application in unconstrained optimization
… 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 termination …
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Scaling rank-one updating formula and its application in unconstrained optimization
… 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 termination …
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Least Relative Change Quasi-Newton Updates for Chemical Engineering Process Optimization
… problems. However, the progress in the area of large-scale optimization is achieved at a slower pace thanks to difficulties in approximation of the sparse symmetric Hessian matrix by using traditional sparse least absolute change quasi-Newton updates.
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Combinatorial Optimization On Massive Datasets: Streaming, Distributed, And Massively Parallel Computation
… there is a rapidly growing need to solve various optimization tasks over such datasets. This in turn raises the following fundamental question: How well can we solve a large-scale optimization problem on massive datasets in a resource-efficient manner? The focus of this thesis is on answering this …
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Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization
Global optimization of expensive, derivative-free black-box functions requires extreme sample efficiency. While Bayesian optimization (BO) is the current state-of-the-art, its performance hinges on surrogate and acquisition function hyperparameters that are often hand-tuned and fail to generalize …
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Analysis of a nonhierarchical decomposition algorithm
Large scale optimization problems are tractable only if they are somehow decomposed. Hierarchical decompositions are inappropriate for some types of problems and do not parallelize well. Sobieszczanski-Sobieski has proposed a nonhierarchical decomposition strategy for nonlinear constrained …
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The Markov chain Monte Carlo approach to importance sampling in stochastic programming
Stochastic programming models are large-scale optimization problems that are used to facilitate decision-making under uncertainty. Optimization algorithms for such problems need to evaluate the expected future costs of current decisions, often referred to as the recourse function. In practice, this …
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First Order Methods for Large-Scale Sparse Optimization
… and biomedical imaging systems. Many important large-scale applications can be modeled as optimization problems with millions of decision variables. Very often, the desired solution is sparse in some form, either because the optimal solution is indeed sparse, or because a sparse solution has …
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Advances and applications in high-dimensional heuristic optimization
… real-world decision scenarios, multiobjective optimization is an area of multicriteria decision-making that seeks to simultaneously optimize two or more conflicting objectives. In contrast to single-objective scenarios, nontrivial multiobjective optimization problems are characterized by a set …
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