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09/19/2023
A Novel Gradient Methodology with Economical Objective Function Evaluations for Data Science Applications
Gradient methods are experiencing a growth in methodological and theoret...
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08/09/2022
Towards Practical Large-scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification
As the scale of problems and data used for experimental design, signal p...
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04/04/2022
Randomized Block Adaptive Linear System Solvers
Randomized linear solvers leverage randomization to structure-blindly co...
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01/15/2022
Residual Tracking and Stopping for Iterative Random Sketching
Iterative random sketching (IRS) offers a computationally expedient appr...
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10/04/2021
Global Convergence and Stability of Stochastic Gradient Descent
In machine learning, stochastic gradient descent (SGD) is widely deploye...
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04/10/2021
Convergence of Adaptive, Randomized, Iterative Linear Solvers
Deterministic and randomized, row-action and column-action linear solver...
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11/05/2020
Stochastic Approximation for High-frequency Observations in Data Assimilation
With the increasing penetration of high-frequency sensors across a numbe...
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04/01/2020
Stopping Criteria for, and Strong Convergence of, Stochastic Gradient Descent on Bottou-Curtis-Nocedal Functions
While Stochastic Gradient Descent (SGD) is a rather efficient algorithm ...
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09/14/2017
The Impact of Local Geometry and Batch Size on the Convergence and Divergence of Stochastic Gradient Descent
Stochastic small-batch (SB) methods, such as mini-batch Stochastic Gradi...
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02/01/2017
On SGD's Failure in Practice: Characterizing and Overcoming Stalling
Stochastic Gradient Descent (SGD) is widely used in machine learning pro...
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12/03/2015