47 citations · 232 across the 24 of their papers we have counts for
27 papers
Inexact Proximal-Gradient Methods with Support Identification
Yutong Dai, Daniel P. Robinson
We consider the proximal-gradient method for minimizing an objective function that is the sum of a smooth function and a non-smooth convex function. A feature that distinguishes ou…
Worst-Case Complexity of an SQP Method for Nonlinear Equality Constrained Stochastic Optimization
Frank E. Curtis, Michael J. O'Neill, Daniel P. Robinson
A worst-case complexity bound is proved for a sequential quadratic optimization (commonly known as SQP) algorithm that has been designed for solving optimization problems involving…
Boosting RANSAC via Dual Principal Component Pursuit
Yunchen Yang, Xinyue Zhang, Tianjiao Ding +3
In this paper, we revisit the problem of local optimization in RANSAC. Once a so-far-the-best model has been found, we refine it via Dual Principal Component Pursuit (DPCP), a robu…
Inexact Sequential Quadratic Optimization for Minimizing a Stochastic Objective Function Subject to Deterministic Nonlinear Equality Constraints
Frank E. Curtis, Daniel P. Robinson, Baoyu Zhou
An algorithm is proposed, analyzed, and tested experimentally for solving stochastic optimization problems in which the decision variables are constrained to satisfy equations defi…
A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear Equality Constrained Optimization with Rank-Deficient Jacobians
Albert S. Berahas, Frank E. Curtis, Michael J. O'Neill +1
A sequential quadratic optimization algorithm is proposed for solving smooth nonlinear equality constrained optimization problems in which the objective function is defined by an e…
A Subspace Acceleration Method for Minimization Involving a Group Sparsity-Inducing Regularizer
Frank E. Curtis, Yutong Dai, Daniel P. Robinson
We consider the problem of minimizing an objective function that is the sum of a convex function and a group sparsity-inducing regularizer. Problems that integrate such regularizer…