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 18 of 18 for “"Block Coordinate descent"”.
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Algorithms for matrix completion
… algorithms. We introduce a scalable primal-dual block coordinate descent algorithm for large sparse matrix completion. The algorithm explicitly maintains a sparse dual and the corresponding low rank primal solution at the same time. Preliminary empirical results illustrate both the scalability …
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Differential Dependency Network and Data Integration for Detecting Network Rewiring and Biomarkers
… Secondly, the computational time of the block coordinate descent algorithm in DDN increases rapidly with the number of involved samples and molecular entities. To address the imbalanced sample group problem, we propose a sample-scale-wide normalized formulation to correct systematic bias …
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Temporally Feathered Radiation Therapy under Uncertainty
… To solve the resulting models, we design block coordinate descent algorithms and demonstrate their performance across applications in stochastic TFRT, portfolio management, and capacity expansion.
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Convex relaxation methods for graphical models : Lagrangian and maximum entropy approaches
… graph into tractable subgraphs, such as small "blocks" of nodes, embedded trees or thin subgraphs. We develop a distributed, iterative algorithm that minimizes the Lagrangian dual function by block coordinate descent. This results in an iterative marginal-matching procedure that enforces …
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Analyzing intentions from big data traces of human activities
… objectives, we design an accelerated stochastic block coordinate descent method with optimal sampling; for optimizing non-strongly convex objectives, we design a stochastic variance reduced alternating direction method of multipliers with the doubling-trick. Inevitably, human activities are …
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Challenges in recommender systems : scalability, privacy, and structured recommendations
… low rank primal solution. We provide a new dual block coordinate descent algorithm for solving the dual problem with a few spectral constraints. Empirical results illustrate the effectiveness of our method in comparison to recently proposed alternatives. In addition, we extend the method to …
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Successive convex approximation: analysis and applications
The block coordinate descent (BCD) method is widely used for minimizing a continuous function f of several block variables. At each iteration of this method, a single block of variables is optimized, while the remaining variables are held fixed. To ensure the convergence of the BCD method, the …
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Modeling and Characterization of Dynamic Changes in Biological Systems from Multi-platform Genomic Data
… and introduce an efficient implementation by the block coordinate descent algorithm. Another type of dynamic changes in biological networks is the observation that a group of genes involved in certain biological functions or processes coordinate to response to outside stimuli, producing distinct …
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Large scale optimization for machine learning
… optimization algorithms like stochastic gradient descent (SGD) in a distributed system raises some issues like synchronization since they were not designed for this purpose. Synchronization is required because consistency should be guaranteed, i.e., the parameters in different machines should be …
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Optimization of Markov Random Fields in Computer Vision
… dual of each proximal problem is optimized via block-coordinate descent. We show that each block of variables can be optimized in a time linear in the number of pixels and labels. Consequently, our algorithm enables efficient and effective optimization of dense CRFs with Gaussian pairwise …
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Optimization of Markov Random Fields in Computer Vision
… dual of each proximal problem is optimized via block-coordinate descent. We show that each block of variables can be optimized in a time linear in the number of pixels and labels. Consequently, our algorithm enables efficient and effective optimization of dense CRFs with Gaussian pairwise …
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Computationally Efficient Methods for High-Dimensional Statistical Problems
… over each variable, and is embedded within a block coordinate descent algorithm. This allows fitting of such models quickly on a laptop computer in a memory efficient manner. The scaling requirements sufficient for this method to recover the correct groups cannot be relaxed for any estimator; …
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Constrained Matrix and Tensor Factorization: Theory, Algorithms, and Applications
… AO framework exploits recent developments in block coordinate descent (BCD)-type methods which help ensure that every limit point is a stationary point, as well as faster and more robust convergence in practice. Extensive simulations and experiments with real data are used to showcase the …
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Unmanned Aerial Vehicles and Edge Computing in Wireless Networks
… ground base station (BS) and mobile users are blocked due to obstacles in the urban environment. SARIS assists the BS in reflecting the signals to randomly distributed mobile users. The results show that the proposed SARIS network significantly improves the weighted sum-rate for ground users, …
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Optimization Algorithms for Structured Machine Learning and Image Processing Problems
… First, we propose a general version of the Block Coordinate Descent (BCD) algorithm for the Group Lasso that employs an efficient approach for optimizing each subproblem exactly. We show that it exhibits excellent performance when the groups are of moderate size. For groups of large size, we …
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Advanced imaging via multiplexed sensing and compressive sensing
… algorithm (alternating direction method and block coordinate descent) is developed for ROSL and a random sampling algorithm is introduced to further accelerate ROSL such that ROSL+ has linear complexity of the matrix size. Extensive evaluations demonstrate that ROSL and ROSL+ achieve the …
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Advancing RIS Optimization: From Ideal to Realistic Models
… change, and then utilize that to design a robust block-coordinate descent (BCD) algorithmic framework which maximizes a lower bound on channel capacity while accounting for imperfect channel state information (CSI). In this framework, the original problem is split into two sub-problems: a) receive …
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Adaptive sparse representations and their applications
… highly undersampled measurements. The proposed block coordinate descent type algorithms involve highly efficient closed-form optimal updates. Importantly, we prove that although the proposed blind compressed sensing formulations are highly nonconvex, our algorithms converge to the set of …