383 citations · 432 across the 16 of their papers we have counts for
10 papers · 1 filter
A Feasible Direction Method with Active-Set Identification for Derivative-Free Optimization
Jeffrey Larson, Matt Menickelly, Lara Zebiane
We propose a model-based trust region method for derivative-free optimization with simulation-based inequality constraints. We are motivated by settings of simulation optimization…
Birkhoff interpolation models for optimization with some available derivatives
Jeffrey Larson, Matt Menickelly, Evan Toler
We consider interpolation-based derivative-free optimization in settings where only some derivatives are available. Such situations arise in scientific computing applications invol…
Importance Sampling in Expensive Finite-Sum Optimization via Contextual Bandit Methods
Matt Menickelly
In computational science workflows, it is often the case that 1) objective functions for optimization involve multiple simulation outputs, and 2) those simulations can be performed…
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…
Structure-Aware Methods for Expensive Derivative-Free Nonsmooth Composite Optimization
Jeffrey Larson, Matt Menickelly
We present new methods for solving a broad class of bound-constrained nonsmooth composite minimization problems. These methods are specially designed for objectives that are some k…
Stochastic Average Model Methods
Matt Menickelly, Stefan M. Wild
We consider the solution of finite-sum minimization problems, such as those appearing in nonlinear least-squares or general empirical risk minimization problems. We are motivated b…
Manifold Sampling for Optimizing Nonsmooth Nonconvex Compositions
Jeffrey Larson, Matt Menickelly, Baoyu Zhou
We propose a manifold sampling algorithm for minimizing a nonsmooth composition , where we assume is nonsmooth and may be inexpensively computed in closed form and…
Tuning Multigrid Methods with Robust Optimization
Jed Brown, Yunhui He, Scott MacLachlan +2
Local Fourier analysis is a useful tool for predicting and analyzing the performance of many efficient algorithms for the solution of discretized PDEs, such as multigrid and domain…
Derivative-free optimization methods
Jeffrey Larson, Matt Menickelly, Stefan M. Wild
In many optimization problems arising from scientific, engineering and artificial intelligence applications, objective and constraint functions are available only as the output of…
Robust Learning of Trimmed Estimators via Manifold Sampling
Matt Menickelly, Stefan M. Wild
We adapt a manifold sampling algorithm for the nonsmooth, nonconvex formulations of learning that arise when imposing robustness to outliers present in the training data. We demons…