Browsing Computer Science by Subject "optimization"
Now showing items 1-13 of 13
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ClaferMPS: Modeling and Optimizing Automotive Electric/Electronic Architectures Using Domain-Specific Languages
(University of Waterloo, 2017-01-23)Modern automotive electric/electronic (E/E) architectures are growing to the point where architects can no longer manually predict the effects of their design decisions. Thus, in addition to applying an architecture reference ... -
Complexity of Sublinear Algorithms for Convexity in Higher Dimensions
(University of Waterloo, 2022-07-19)Convexity plays a prominent role in both mathematics and computer science. It is defined for sets and functions, and many related problems can be solved efficiently if the set/function is convex. In this thesis, we focus ... -
Local Graph Clustering Using l1-regularized PageRank Algorithms
(University of Waterloo, 2020-05-05)Local graph clustering methods are used to find small- and medium-scale clusters without traversing the graph. It has been shown that the combination of Approximate Personalized PageRank (APPR) algorithm and sweep method ... -
Matrix Polynomials and their Lower Rank Approximations
(University of Waterloo, 2019-08-07)This thesis is a wide ranging work on computing a “lower-rank” approximation of a matrix polynomial using second-order non-linear optimization techniques. Two notions of rank are investigated. The first is the rank as the ... -
Modelling, Design, and Control of Energy Systems: A Data-Driven Approach
(University of Waterloo, 2019-09-23)In 2018, nearly two-thirds of newly installed global power generation has come from renewable energy sources. Distributed installations of solar photovoltaic (PV) panels have been at the forefront of this global energy ... -
Naive Bayes Data Complexity and Characterization of Optima of the Unsupervised Expected Likelihood
(University of Waterloo, 2017-09-21)The naive Bayes model is a simple model that has been used for many decades, often as a baseline, for both supervised and unsupervised learning. With a latent class variable it is one of the simplest latent variable models, ... -
On the Control of Active End-nodes in the Smart Grid
(University of Waterloo, 2015-08-11)The electrical grid has substantially changed in recent years due to the integration of several disruptive load and generation technologies into low-voltage distribution networks, which are meant to smarten it and improve ... -
Optimization for Image Segmentation
(University of Waterloo, 2019-06-26)Image segmentation, i.e., assigning each pixel a discrete label, is an essential task in computer vision with lots of applications. Major techniques for segmentation include for example Markov Random Field (MRF), Kernel ... -
Optimization Methods for Semi-Supervised Learning
(University of Waterloo, 2018-05-17)The goal of this thesis is to provide efficient optimization algorithms for some semi-supervised learning (SSL) tasks in machine learning. For many machine learning tasks, training a classifier requires a large amount of ... -
Quantifying the Effects of Solar Panel Orientation on the Electrical Grid
(University of Waterloo, 2016-11-08)As the prices of solar panels continue to decline, energy production from solar farms is skyrocketing, leading to a situation where, at certain times, solar farms produce more energy than can be consumed. Today, the only ... -
Resource optimization for fault-tolerant quantum computing
(University of Waterloo, 2014-01-02)Quantum computing offers the potential for efficiently solving otherwise classically difficult problems, with applications in material and drug design, cryptography, theoretical physics, number theory and more. However, ... -
Type-Aware Optimizations with Imperfect Types
(University of Waterloo, 2024-05-10)JavaScript, a programming language originally designed for web browsers, has become ubiquitous, experiencing adoption across multiple platforms. Its dynamic type system and prototype-based object orientation are well-known ... -
Wasserstein Adversarial Robustness
(University of Waterloo, 2020-09-21)Deep models, while being extremely flexible and accurate, are surprisingly vulnerable to ``small, imperceptible'' perturbations known as adversarial attacks. While the majority of existing attacks focus on measuring ...