33 citations · 116 across the 18 of their papers we have counts for
5 papers · 1 filter
Zeroth-Order Regularized Optimization (ZORO): Approximately Sparse Gradients and Adaptive Sampling
HanQin Cai, Daniel Mckenzie, Wotao Yin +1
We consider the problem of minimizing a high-dimensional objective function, which may include a regularization term, using (possibly noisy) evaluations of the function. Such optim…
Submodular Optimization Problems and Greedy Strategies: A Survey
Yajing Liu, Edwin K. P. Chong, Ali Pezeshki +1
The greedy strategy is an approximation algorithm to solve optimization problems arising in decision making with multiple actions. How good is the greedy strategy compared to the o…
A General Framework for Bounding Approximate Dynamic Programming Schemes
Yajing Liu, Edwin Chong, Ali Pezeshki +1
For years, there has been interest in approximation methods for solving dynamic programming problems, because of the inherent complexity in computing optimal solutions characterize…
Performance Bounds with Curvature for Batched Greedy Optimization
Yajing Liu, Zhenliang Zhang, Edwin K. P. Chong +1
The batched greedy strategy is an approximation algorithm to maximize a set function subject to a matroid constraint. Starting with the empty set, the batched greedy strategy itera…
Performance Bounds for the -Batch Greedy Strategy in Optimization Problems with Curvature
Yajing Liu, Zhenliang Zhang, Edwin K. P. Chong +1
The -batch greedy strategy is an approximate algorithm to solve optimization problems where the optimal solution is hard to obtain. Starting with the empty set, the -batch gr…