1 citations · 1 across the 4 of their papers we have counts for
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Gradient Descent and the Power Method: Exploiting their connection to find the leftmost eigen-pair and escape saddle points
Rachael Tappenden, Martin Takáč
This work shows that applying Gradient Descent (GD) with a fixed step size to minimize a (possibly nonconvex) quadratic function is equivalent to running the Power Method (PM) on t…
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm
Majid Jahani, Mohammadreza Nazari, Rachael Tappenden +2
This work presents a new algorithm for empirical risk minimization. The algorithm bridges the gap between first- and second-order methods by computing a search direction that uses…
Gradient and Hessian approximations in Derivative Free Optimization
Ian D. Coope, Rachael Tappenden
This work investigates finite differences and the use of interpolation models to obtain approximations to the first and second derivatives of a function. Here, it is shown that if…
Dual Free Adaptive Minibatch SDCA for Empirical Risk Minimization
Xi He, Rachael Tappenden, Martin Takac
In this paper we develop an adaptive dual free Stochastic Dual Coordinate Ascent (adfSDCA) algorithm for regularized empirical risk minimization problems. This is motivated by the…
A Flexible ADMM Algorithm for Big Data Applications
Daniel P. Robinson, Rachael E. H. Tappenden
We present a flexible Alternating Direction Method of Multipliers (F-ADMM) algorithm for solving optimization problems involving a strongly convex objective function that is separa…