1 citations · 2 across the 10 of their papers we have counts for
10 papers
Adaptive Sampling Trust Region Optimization for Derivative-free Stochastic Functions and Deterministic Equality Constraints
Nicole Felice, Sara Shashaani, Lindon Roberts
We study optimization problems with noisy zeroth-order objective observations and deterministic nonlinear equality constraints with available derivatives. We propose a constrained…
Extrapolation-based Direct Search for Nonsmooth Stochastic Zeroth-Order Optimization
Anthony Palmieri, Francesco Rinaldi, Sara Shashaani
We propose and analyze a stochastic direct-search method for unconstrained zeroth-order minimization of locally Lipschitz, possibly nonsmooth, objectives. The method combines rando…
Adaptive Regularization within Trust Region Methods for Stochastic Nonconvex Optimization
Yunsoo Ha, Sara Shashaani, Quoc Tran-dinh
We propose a stochastic nonconvex optimization algorithm that achieves almost sure iteration complexity for problems with smooth objective functions…
Stratified adaptive sampling for derivative-free stochastic trust-region optimization
Giovanni Amici, Sara Shashaani, Pranav Jain
There is emerging evidence that trust-region (TR) algorithms are very effective at solving derivative-free nonconvex stochastic optimization problems in which the objective functio…
Root Finding and Metamodeling for Rapid and Robust Computer Model Calibration
Yongseok Jeon, Sara Shashaani
We concern computer model calibration problem where the goal is to find the parameters that minimize the discrepancy between the multivariate real-world and computer model outputs.…
Complexity of Zeroth- and First-order Stochastic Trust-Region Algorithms
Yunsoo Ha, Sara Shashaani, Raghu Pasupathy
Model update (MU) and candidate evaluation (CE) are classical steps incorporated inside many stochastic trust-region (TR) algorithms. The sampling effort exerted within these steps…
Building Trees for Probabilistic Prediction via Scoring Rules
Sara Shashaani, Ozge Surer, Matthew Plumlee +1
Decision trees built with data remain in widespread use for nonparametric prediction. Predicting probability distributions is preferred over point predictions when uncertainty play…
Two-Stage Estimation and Variance Modeling for Latency-Constrained Variational Quantum Algorithms
Yunsoo Ha, Sara Shashaani, Matt Menickelly
The Quantum Approximate Optimization Algorithm (QAOA) has enjoyed increasing attention in noisy intermediate-scale quantum computing due to its application to combinatorial optimiz…
Simulation Model Calibration with Dynamic Stratification and Adaptive Sampling
Pranav Jain, Sara Shashaani, Eunshin Byon
Calibrating simulation models that take large quantities of multi-dimensional data as input is a hard simulation optimization problem. Existing adaptive sampling strategies offer a…
Uncertainty Quantification using Simulation Output: Batching as an Inferential Device
Yongseok Jeon, Yi Chu, Raghu Pasupathy +1
We present batching as an omnibus device for uncertainty quantification using simulation output. We consider the classical context of a simulationist performing uncertainty quantif…