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 61 for “"Integer Optimization"”.
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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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Integer optimization methods for machine learning
In this thesis, we propose new mixed integer optimization (MIO) methods to ad- dress problems in machine learning. The first part develops methods for supervised bipartite ranking, which arises in prioritization tasks in diverse domains such as information retrieval, recommender systems, natural …
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Multi-target tracking via mixed integer optimization
… to these approaches, we propose the use of mixed integer optimization (MIO) models and local search algorithms that are (a) scalable, as they provide near optimal solutions for six targets and ten time periods in milliseconds to seconds, (b) general, as they make no assumptions on the data, (c) …
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Air Force Crew Scheduling: An Integer Optimization Approach
… development process. We develop multiple optimization formulations for the Air Force crew scheduling problem. Furthermore, we present multiple objective functions aiming at mimicking reality to account for pilot qualification upgrades and their ability to stay current and mission ready. To …
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Learning contact-aware robot controllers from mixed integer optimization
… guarantees of global optimality through mixed-integer programming. That method is applied successfully to a humanoid robot in laboratory conditions, but proves difficult to rely on when the robot is experiences unmodeled disturbances. To overcome those limitations, this thesis also introduces …
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New Trust Region SQP Methods for Continuous and Integer Optimization
… algorithms are presented that address nonlinear optimization problems. The algorithms belong to the class of sequential quadratic programming (SQP) methods. Two problem formulations that arise frequently in real-world applications are considered. Both have in common that functions are nonlinear …
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Large-Scale Airline Recovery using Mixed-Integer Optimization and Supervised Learning
… prevent airlines from using a full-scale optimization approach. This thesis proposes fast solution methods that combine mixed-integer optimization and supervised machine learning techniques to find better solutions to large-scale airline recovery problems than those found with other exact …
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Computational experiments for local search algorithms for binary and mixed integer optimization
… we implement and test two algorithms for binary optimization and mixed integer optimization, respectively. We fine tune the parameters of these two algorithms and achieve satisfactory performance. We also compare our algorithms with CPLEX on large amount of fairly large-size instances. Based on …
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Novel Integer Optimization Methods and their Applications in Biomass Supply Chain and Power Dominating Set
Integer optimization (IO) problems arise in research areas and our daily life almost in every aspect. IO formulations and methods can be adopted to make optimal decisions for solution-searching and management with global optimization. Two main areas of applications of IO are studied in the …
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Regression under a modern optimization lens
… years (1990-2014), algorithmic advances in integer optimization combined with hardware improvements have resulted in an astonishing 200 billion factor speedup in solving mixed integer optimization (MIO) problems. The common mindset of MIO as theoretically elegant but practically irrelevant …
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From data to decisions in healthcare : an optimization perspective
… methodological and technological advances in optimization, statistics, and machine learning. What is still not well understood is how to combine these tools to take data as inputs and give decisions as outputs. The healthcare arena offers fertile ground for improvement in data-driven …
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Robust reconnaissance asset planning under uncertainty
… collection value. We propose a deterministic integer optimization formulation and two robust mixed-integer optimization extensions to address this problem. Robustness is applied to our model using both polyhedral and ellipsoidal uncertainty sets resulting in tractable mixed integer linear and …
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Messaging for large-scale distributed computation with factor graphs
… of our Factor Graph Computing framework: (a) Integer Optimization for hard problems, (b) Page-Rank, and (c) Singular Value Decomposition (SVD). We implement Factor Graph Computing on top of two different PubSub systems: Redis's out-of-the-box PubSub and a PubSub that we have built on top of …
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Sparsity in Machine Learning: Theory and Applications
… reduction of operational and investment costs. Integer optimization is a highly effective tool in the conception of methods to tackle sparsity. It offers a rigorous framework to build sparse models and has proved to provide more accurate and sparse models than other approaches including the ones …
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Enabling massive parallelism for two-stage stochastic integer optimizations a branch and bound based approach
… and commensurate cost savings. Stochastic optimization techniques are used to incorporate uncertainty in the data to arrive at robust resource allocations. The application of stochastic optimization extends to a broad range of areas ranging from finance to production to economics to energy …
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Optimization of tensegrity structures
This thesis presents a new approach to solve the optimization of articulated structures and especially looks into the performance of tensegrity systems compared to regular trusses. Volume is the objective to minimize and a wide range of constraints can be considered from the required mechanical …
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School choice : a discrete optimization approach
… and transportation costs. Facing this intricate optimization problem, school districts often utilize to stable-matching techniques which only produce stable matchings that do not incorporate these different objectives; this can be expensive and inequitable. We present a new optimization model for …
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BUBLS : a mixed integer program for transit centre location in the Lower Mainland
A mixed integer optimization model is developed to determine both the optimal location of transit centres to serve BC Transit's Lower Mainland route network and the optimal allocation of buses to those centres. The existing five transit centres are explored as well as five candidate facilities. The …
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Robust transportation network design under user equilibrium
… Under deterministic demand, we propose an exact integer optimization approach that leads to a quadratic objective, linear constraints optimization problem. As a result, the problem is efficiently solvable via commercial software, when the costs are linear functions of traffic flows. We then use …
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