12 citations · 26 across the 18 of their papers we have counts for
22 papers
Porting the Nonlinear Optimization Library HiOp to Accelerator-Based Hardware Architectures
Slaven Peles, Kalyan S. Perumalla, Maksudul Alam +4
While interior point methods have been the centerpiece of nonlinear programming tools used in science and engineering, their reliance on linear solvers that can tackle sparse symme…
Practical Efficient Global Optimization is No-regret
Jingyi Wang, Haowei Wang, Nai-Yuan Chiang +3
Efficient global optimization (EGO) is one of the most widely used noise-free Bayesian optimization algorithms.It comprises the Gaussian process (GP) surrogate model and expected i…
Bayesian Optimization with Expected Improvement: No Regret and the Choice of Incumbent
Jingyi Wang, Haowei Wang, Szu Hui Ng +1
Expected improvement (EI) is one of the most widely used acquisition functions in Bayesian optimization (BO). Despite its proven empirical success in applications, the cumulative r…
Algebraic Multigrid with Filtering: An Efficient Preconditioner for Interior Point Methods in Large-Scale Contact Mechanics Optimization
Socratis Petrides, Tucker Hartland, Tzanio Kolev +6
Large-scale contact mechanics simulations are crucial in many engineering fields such as structural design and manufacturing. In the frictionless case, contact can be modeled by mi…
Convergence Rates of Constrained Expected Improvement
Haowei Wang, Jingyi Wang, Zhongxiang Dai +3
Constrained Bayesian optimization (CBO) methods have seen significant success in black-box optimization with constraints. One of the most commonly used CBO methods is the constrain…
On the convergence rate of noisy Bayesian Optimization with Expected Improvement
Jingyi Wang, Haowei Wang, Nai-Yuan Chiang +1
Expected improvement (EI) is one of the most widely used acquisition functions in Bayesian optimization (BO). Despite its proven success in applications for decades, important open…
On Improved Regret Bounds In Bayesian Optimization with Gaussian Noise
Jingyi Wang, Haowei Wang, Cosmin G. Petra +1
Bayesian optimization (BO) with Gaussian process (GP) surrogate models is a powerful black-box optimization method. Acquisition functions are a critical part of a BO algorithm as t…
A Scalable Interior-Point Gauss-Newton Method for PDE-Constrained Optimization with Bound Constraints
Tucker Hartland, Cosmin G. Petra, Noemi Petra +1
We present a scalable approach to solve a class of elliptic partial differential equation (PDE)-constrained optimization problems with bound constraints. This approach utilizes a r…
Design optimization in unilateral contact using pressure constraints and Bayesian optimization
Jingyi Wang, Jerome Solberg, Mike A. Puso +2
Design optimization problems, e.g., shape optimization, that involve deformable bodies in unilateral contact are challenging as they require robust contact solvers, complex optimiz…
Local convergence of a sequential quadratic programming method for a class of nonsmooth nonconvex objectives
Jingyi Wang, Cosmin G. Petra
A sequential quadratic programming (SQP) algorithm is designed for nonsmooth optimization problems with upper-C^2 objective functions. Upper-C^2 functions are locally equivalent to…
A sequential quadratic programming method for nonsmooth stochastic optimization with upper-C^2 objective
J. Wang, I. Aravena, C. G. Petra
We propose a sequential quadratic programming (SQP) method that can incorporate adaptive sampling for stochastic nonsmooth nonconvex optimization problems with upper-C^2 objectives…
Compact representations of structured BFGS matrices
Johannes J. Brust, Zichao, Di +2
For general large-scale optimization problems compact representations exist in which recursive quasi-Newton update formulas are represented as compact matrix factorizations. For pr…
Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition
Ignacio Aravena, Daniel K. Molzahn, Shixuan Zhang +12
The optimal power flow problem is central to many tasks in the design and operation of electric power grids. This problem seeks the minimum cost operating point for an electric pow…
An Optimization algorithm for nonsmooth nonconvex problems with upper-C^2 objective
Jingyi Wang, Cosmin G. Petra
An optimization algorithm for nonsmooth nonconvex constrained optimization problems with upper-C2 objective functions is proposed and analyzed. Upper-C2 is a weakly concave propert…
A simplified nonsmooth nonconvex bundle method with applications to security-constrained ACOPF problems
Jingyi Wang, Cosmin G. Petra
An optimization algorithm for a group of nonsmooth nonconvex problems inspired by two-stage stochastic programming problems is proposed. The main challenges for these problems incl…
A Hybrid Direct-Iterative Method for Solving KKT Linear Systems
Shaked Regev, Nai-Yuan Chiang, Eric Darve +4
We propose a solution strategy for linear systems arising in interior method optimization, which is suitable for implementation on hardware accelerators such as graphical processin…
Solving realistic security-constrained optimal power flow problems
Cosmin G. Petra, Ignacio Aravena
We present a decomposition approach for obtaining good feasible solutions for the security-constrained alternating-current optimal power flow (SCACOPF) problem at an industrial sca…
Another source of mesh dependence in topology optimization
Miguel A. Salazar de Troya, Geoffrey M. Oxberry, Cosmin G. Petra +1
The topology optimization community has regularly employed nonlinear programming (NLP) algorithms from the operations research community. However, these algorithms are implemented…
Large-scale Optimization with Linear Equality Constraints using Reduced Compact Representation
Johannes J. Brust, Roummel F. Marcia, Cosmin G. Petra +1
For optimization problems with linear equality constraints, we prove that the (1,1) block of the inverse KKT matrix remains unchanged when projected onto the nullspace of the const…
Statistical Treatment of Inverse Problems Constrained by Differential Equations-Based Models with Stochastic Terms
Emil M. Constantinescu, Noemi Petra, Julie Bessac +1
This paper introduces a statistical treatment of inverse problems constrained by models with stochastic terms. The solution of the forward problem is given by a distribution repres…
A Bayesian Approach for Parameter Estimation with Uncertainty for Dynamic Power Systems
Noemi Petra, Cosmin G. Petra, Zheng Zhang +2
We address the problem of estimating the uncertainty in the solution of power grid inverse problems within the framework of Bayesian inference. We investigate two approaches, an ad…
Multigrid preconditioning of linear systems for interior point methods applied to a class of box-constrained optimal control problems
Andrei Draganescu, Cosmin Petra
In this article we construct and analyze multigrid preconditioners for discretizations of operators of the form D+K* K, where D is the multiplication with a relatively smooth posit…