Frank E. Curtis
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View article: Equity-promoting integer programming approaches for medical resident rotation scheduling
Equity-promoting integer programming approaches for medical resident rotation scheduling Open
Motivated by our collaboration with a residency program at an academic health system, we propose new integer programming (IP) approaches for the r esident-to- r otation a ssignment p roblem (RRAP). Given sets of residents, resident classes…
View article: Progressively Sampled Equality-Constrained Optimization
Progressively Sampled Equality-Constrained Optimization Open
An algorithm is proposed, analyzed, and tested for solving continuous nonlinear-equality-constrained optimization problems where the constraints are defined by an expectation or an average over a large (finite) number of terms. The main id…
View article: Active-Set Identification in Noisy and Stochastic Optimization
Active-Set Identification in Noisy and Stochastic Optimization Open
Identifying active constraints from a point near an optimal solution is important both theoretically and practically in constrained continuous optimization, as it can help identify optimal Lagrange multipliers and essentially reduces an in…
View article: Fair Supervised Learning Through Constraints on Smooth Nonconvex Unfairness-Measure Surrogates
Fair Supervised Learning Through Constraints on Smooth Nonconvex Unfairness-Measure Surrogates Open
A new strategy for fair supervised machine learning is proposed. The main advantages of the proposed strategy as compared to others in the literature are as follows. (a) We introduce a new smooth nonconvex surrogate to approximate the Heav…
View article: NonOpt: Nonconvex, Nonsmooth Optimizer
NonOpt: Nonconvex, Nonsmooth Optimizer Open
NonOpt, a C++ software package for minimizing locally Lipschitz objective functions, is presented. The software is intended primarily for minimizing objective functions that are nonconvex and/or nonsmooth. The package has implementations o…
View article: An Interior-Point Algorithm for Continuous Nonlinearly Constrained Optimization with Noisy Function and Derivative Evaluations
An Interior-Point Algorithm for Continuous Nonlinearly Constrained Optimization with Noisy Function and Derivative Evaluations Open
An algorithm based on the interior-point methodology for solving continuous nonlinearly constrained optimization problems is proposed, analyzed, and tested. The distinguishing feature of the algorithm is that it presumes that only noisy va…
View article: Almost-Sure Convergence of Iterates and Multipliers in Stochastic Sequential Quadratic Optimization
Almost-Sure Convergence of Iterates and Multipliers in Stochastic Sequential Quadratic Optimization Open
Stochastic sequential quadratic optimization (SQP) methods for solving continuous optimization problems with nonlinear equality constraints have attracted attention recently, such as for solving large-scale data-fitting problems subject to…
View article: Using Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learning
Using Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learning Open
In distributed computing environments, collaborative machine learning enables multiple clients to train a global model collaboratively. To preserve privacy in such settings, a common technique is to utilize frequent updates and transmissio…
View article: Single-Loop Deterministic and Stochastic Interior-Point Algorithms for Nonlinearly Constrained Optimization
Single-Loop Deterministic and Stochastic Interior-Point Algorithms for Nonlinearly Constrained Optimization Open
An interior-point algorithm framework is proposed, analyzed, and tested for solving nonlinearly constrained continuous optimization problems. The main setting of interest is when the objective and constraint functions may be nonlinear and/…
View article: Insights and Innovations from the SSMCDAT 2023: Bridging Solid-State Materials Chemistry and Data Science
Insights and Innovations from the SSMCDAT 2023: Bridging Solid-State Materials Chemistry and Data Science Open
The Solid-State Materials Chemistry Data Science Hackathon (SSMCDAT), held at Lehigh University from January 19–21, 2023, demonstrated the power of interdisciplinary collaboration in tackling challenges in solid-state materials chemistry. …
View article: A Stochastic Inexact Sequential Quadratic Optimization Algorithm for Nonlinear Equality-Constrained Optimization
A Stochastic Inexact Sequential Quadratic Optimization Algorithm for Nonlinear Equality-Constrained Optimization Open
A stochastic algorithm is proposed, analyzed, and tested experimentally for solving continuous optimization problems with nonlinear equality constraints. It is assumed that constraint function and derivative values can be computed but that…
View article: Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition
Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition Open
Intro to the ARPA-E Grid Optimization Competition In “Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition,” we review the state of the art in practical algo…
View article: Almost-sure convergence of iterates and multipliers in stochastic sequential quadratic optimization
Almost-sure convergence of iterates and multipliers in stochastic sequential quadratic optimization Open
Stochastic sequential quadratic optimization (SQP) methods for solving continuous optimization problems with nonlinear equality constraints have attracted attention recently, such as for solving large-scale data-fitting problems subject to…
View article: A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems
A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems Open
A stochastic-gradient-based interior-point algorithm for minimizing a continuously differentiable objective function (that may be nonconvex) subject to bound constraints is presented, analyzed, and demonstrated through experimental results…
View article: Sequential Quadratic Optimization for Stochastic Optimization with Deterministic Nonlinear Inequality and Equality Constraints
Sequential Quadratic Optimization for Stochastic Optimization with Deterministic Nonlinear Inequality and Equality Constraints Open
A sequential quadratic optimization algorithm for minimizing an objective function defined by an expectation subject to nonlinear inequality and equality constraints is proposed, analyzed, and tested. The context of interest is when it is …
View article: A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization
A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization Open
This paper introduces a new proximal stochastic gradient method with variance reduction and stabilization for minimizing the sum of a convex stochastic function and a group sparsity-inducing regularization function. Since the method may be…
View article: Hybrid Interior-Point/Active-Set SCOPF Algorithms Exploiting Power System Characteristics
Hybrid Interior-Point/Active-Set SCOPF Algorithms Exploiting Power System Characteristics Open
These protected data were produced under agreement no. with the U.S. Department of Energy and may not be published, disseminated, or disclosed to others outside the Government until 5 years after development of information under this agree…
View article: Incremental Quasi-Newton Algorithms for Solving Nonconvex, Nonsmooth, Finite-Sum Optimization Problems
Incremental Quasi-Newton Algorithms for Solving Nonconvex, Nonsmooth, Finite-Sum Optimization Problems Open
Algorithms for solving nonconvex, nonsmooth, finite-sum optimization problems are proposed and tested. In particular, the algorithms are proposed and tested in the context of an optimization problem formulation arising in semi-supervised m…
View article: An inexact column-and-constraint generation method to solve two-stage robust optimization problems
An inexact column-and-constraint generation method to solve two-stage robust optimization problems Open
We propose a new inexact column-and-constraint generation (i-C&CG) method to solve two-stage robust optimization problems. The method allows solutions to the master problems to be inexact, which is desirable when solving large-scale and/or…
View article: Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition
Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition Open
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 power grid while satisfying both engineering requirements and …
View article: Stochastic Optimization Approaches for an Operating Room and Anesthesiologist Scheduling Problem
Stochastic Optimization Approaches for an Operating Room and Anesthesiologist Scheduling Problem Open
We propose combined allocation, assignment, sequencing, and scheduling problems under uncertainty involving multiple operation rooms (ORs), anesthesiologists, and surgeries, as well as methodologies for solving such problems. Specifically,…
View article: Worst-Case Complexity of TRACE with Inexact Subproblem Solutions for Nonconvex Smooth Optimization
Worst-Case Complexity of TRACE with Inexact Subproblem Solutions for Nonconvex Smooth Optimization Open
An algorithm for solving nonconvex smooth optimization problems is proposed, analyzed, and tested. The algorithm is an extension of the Trust Region Algorithm with Contractions and Expansions (TRACE) [Math. Prog. 162(1):132, 2017]. In part…
View article: Gradient Sampling Methods with Inexact Subproblem Solutions and Gradient Aggregation
Gradient Sampling Methods with Inexact Subproblem Solutions and Gradient Aggregation Open
Gradient sampling (GS) methods for the minimization of objective functions that may be nonconvex and/or nonsmooth are proposed, analyzed, and tested. One of the most computationally expensive components of contemporary GS methods is the ne…
View article: Derivative-Free Bound-Constrained Optimization for Solving Structured Problems with Surrogate Models
Derivative-Free Bound-Constrained Optimization for Solving Structured Problems with Surrogate Models Open
We propose and analyze a model-based derivative-free (DFO) algorithm for solving bound-constrained optimization problems where the objective function is the composition of a smooth function and a vector of black-box functions. We assume th…
View article: Worst-Case Complexity of an SQP Method for Nonlinear Equality Constrained Stochastic Optimization
Worst-Case Complexity of an SQP Method for Nonlinear Equality Constrained Stochastic Optimization Open
A worst-case complexity bound is proved for a sequential quadratic optimization (commonly known as SQP) algorithm that has been designed for solving optimization problems involving a stochastic objective function and deterministic nonlinea…
View article: A Decomposition Algorithm for Large-Scale Security-Constrained AC Optimal Power Flow
A Decomposition Algorithm for Large-Scale Security-Constrained AC Optimal Power Flow Open
A decomposition algorithm for solving large-scale security-constrained AC optimal power flow problems is presented. The formulation considered is the one used in the ARPA-E Grid Optimization (GO) Competition, Challenge 1, held from Novembe…
View article: Inexact Sequential Quadratic Optimization for Minimizing a Stochastic Objective Function Subject to Deterministic Nonlinear Equality Constraints
Inexact Sequential Quadratic Optimization for Minimizing a Stochastic Objective Function Subject to Deterministic Nonlinear Equality Constraints Open
An algorithm is proposed, analyzed, and tested experimentally for solving stochastic optimization problems in which the decision variables are constrained to satisfy equations defined by deterministic, smooth, and nonlinear functions. It i…
View article: A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear Equality Constrained Optimization with Rank-Deficient Jacobians
A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear Equality Constrained Optimization with Rank-Deficient Jacobians Open
A sequential quadratic optimization algorithm is proposed for solving smooth nonlinear equality constrained optimization problems in which the objective function is defined by an expectation of a stochastic function. The algorithmic struct…
View article: Trust-Region Newton-CG with Strong Second-Order Complexity Guarantees for Nonconvex Optimization
Trust-Region Newton-CG with Strong Second-Order Complexity Guarantees for Nonconvex Optimization Open
Worst-case complexity guarantees for nonconvex optimization algorithms have been a topic of growing interest. Multiple frameworks that achieve the best known complexity bounds among a broad class of first- and second-order strategies have …