383 citations · 432 across the 16 of their papers we have counts for
17 papers
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…
Edge-Aligned Beam Placement in Scanning Probe Tomography via Reconstruction-Free Sequential Design of Experiments
San Dinh, Zichao Wendy Di, Matt Menickelly
In X-ray scanning probe tomography, reconstruction quality generally improves with larger numbers of projections. However, additional projections increase experiment costs, acquisi…
Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration
Henry Shaowu Yuchi, Michael Grosskopf, Aman Sharma +4
Gaussian processes are widely used for surrogate modeling in computer experiments, which often produce numerous intermediate variables that are not explicitly used in standard cali…
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…
HFBTHO-AD: Differentiation of a nuclear energy density functional code
Laurent Hascoët, Matt Menickelly, Sri Hari Krishna Narayanan +3
The HFBTHO code implements a nuclear energy density functional solver to model the structure of atomic nuclei. HFBTHO has previously been used to calibrate energy functionals and p…
A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm
Kwassi Joseph Dzahini, Jeffrey M. Larson, Matt Menickelly +1
We introduce ANASTAARS, a noise-aware scalable classical optimizer for variational quantum algorithms such as the quantum approximate optimization algorithm (QAOA). ANASTAARS lever…
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…
A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms
Jeffrey Larson, Matt Menickelly, Jiahao Shi
A key component of variational quantum algorithms (VQAs) is the choice of classical optimizer employed to update the parameterization of an ansatz. It is well recognized that quant…
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…
Latency considerations for stochastic optimizers in variational quantum algorithms
Matt Menickelly, Yunsoo Ha, Matthew Otten
Variational quantum algorithms, which have risen to prominence in the noisy intermediate-scale quantum setting, require the implementation of a stochastic optimizer on classical ha…
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…
Optimization and Supervised Machine Learning Methods for Fitting Numerical Physics Models without Derivatives
Raghu Bollapragada, Matt Menickelly, Witold Nazarewicz +3
We address the calibration of a computationally expensive nuclear physics model for which derivative information with respect to the fit parameters is not readily available. Of par…
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…