36 citations · 58 across the 14 of their papers we have counts for
14 papers
Gradient Descent in the Absence of Global Lipschitz Continuity of the Gradients
Vivak Patel, Albert S. Berahas
Gradient descent (GD) is a collection of continuous optimization methods that have achieved immeasurable success in practice. Owing to data science applications, GD with diminishin…
Towards Practical Large-scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification
Nathaniel Pritchard, Vivak Patel
As the scale of problems and data used for experimental design, signal processing and data assimilation grow, the oft-occuring least squares subproblems are correspondingly growing…
Randomized Block Adaptive Linear System Solvers
Vivak Patel, Mohammad Jahangoshahi, Daniel Adrian Maldonado
Randomized linear solvers randomly compress and solve a linear system with compelling theoretical convergence rates and computational complexities. However, such solvers suffer a s…
Solving, Tracking and Stopping Streaming Linear Inverse Problems
Nathaniel Pritchard, Vivak Patel
In large-scale applications including medical imaging, collocation differential equation solvers, and estimation with differential privacy, the underlying linear inverse problem ca…
Global Convergence and Stability of Stochastic Gradient Descent
Vivak Patel, Shushu Zhang, Bowen Tian
In machine learning, stochastic gradient descent (SGD) is widely deployed to train models using highly non-convex objectives with equally complex noise models. Unfortunately, SGD t…
Convergence of Adaptive, Randomized, Iterative Linear Solvers
Vivak Patel, Mohammad Jahangoshahi, Daniel Adrian Maldonado
Deterministic and randomized, row-action and column-action linear solvers have become increasingly popular owing to their simplicity, low computational and memory complexities, and…