Albert S. Berahas

About

Dr. Albert S. Berahas is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan. His research broadly focuses on designing, developing, analyzing, and implementing algorithms to solve large-scale nonlinear optimization problems. His work spans a range of topics within nonlinear optimization, with particular interest in general nonlinear optimization algorithms, optimization algorithms for machine learning, constrained optimization, stochastic optimization, derivative-free optimization, and distributed optimization. Through this diverse portfolio, Dr. Berahas aims to advance both the theoretical foundations and practical performance of optimization methods across a wide range of applications. He has a strong academic background, having earned his PhD in Engineering Sciences and Applied Mathematics from Northwestern University in 2018, after completing his MSc at Northwestern in 2012 and his BSc at Cornell University in 2009. Prior to his current position, he was a Postdoctoral Research Fellow at Lehigh University and Northwestern University.

Research topics

• Computer Science
• Economics
• Mathematical optimization
• Algorithm
• Mathematics

Selected publications

• First- and second-order high probability complexity bounds for trust-region methods with noisy oracles

  Mathematical Programming · 2023-07-29 · 22 citations

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• On the Convergence of Nested Decentralized Gradient Methods with Multiple Consensus and Gradient Steps

  IEEE Transactions on Signal Processing · 2020 · 13 citations

  1st authorCorresponding

  In this paper, we consider minimizing a sum of local convex objective functions in a distributed setting, where the cost of communication and/or computation can be expensive. We extend and generalize the analysis for a class of nested gradient-based distributed algorithms (NEAR-DGD; Berahas, Bollapragada, Keskar and Wei, 2018) to account for multiple gradient steps at every iteration. We show the effect of performing multiple gradient steps on the rate of convergence and on the size of the neigh…

  DOI

• A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear-Equality-Constrained Optimization with Rank-Deficient Jacobians

  Mathematics of Operations Research · 2023-10-30 · 12 citations

  article1st authorCorresponding

  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. The algorithmic structure of the proposed method is based on a step decomposition strategy that is known in the literature to be widely effective in practice, wherein each search direction is computed as the sum of a normal step (toward linearized feasibility) and a tangential step (toward objective decreas…

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• Multiblock Parameter Calibration in Computer Models

  INFORMS Journal on Data Science · 2023-05-12 · 7 citations

  article

  Parameter calibration aims to estimate unobservable parameters used in a computer model by using physical process responses and computer model outputs. In the literature, existing studies calibrate all parameters simultaneously using an entire data set. However, in certain applications, some parameters are associated with only a subset of data. For example, in the building energy simulation, cooling (heating) season parameters should be calibrated using data collected during the cooling (heating…

  PublisherDOI

• Gradient Descent in the Absence of Global Lipschitz Continuity of the Gradients

  SIAM Journal on Mathematics of Data Science · 2024-07-05 · 6 citations

  articleOpen accessSenior author
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Frequent coauthors

• Martin Takáč

  18 shared

• Raghu Bollapragada

  15 shared

• Majid Jahani

  Lehigh University

  10 shared

• Liyuan Cao

  Peking University

  10 shared

• Katya Scheinberg

  Cornell University

  9 shared

• Jorge Nocedal

  9 shared

• Frank E. Curtis

  Lehigh University

  6 shared

• Ermin Wei

  Northwestern University

  6 shared

Education

• PhD, Engineering Sciences and Applied Mathematics

  Northwestern University

  2018

• MSc, Engineering Sciences and Applied Mathematics

  Northwestern University

  2012

• BSc, Operations Research and Industrial Engineering

  Cornell University

  2009

Awards & honors

• 2025 Alpha Pi Mu IOE Outstanding Instructor Award, Departmen…
• 2022 Charles Broyden Prize, Best Paper 2022 Optimization Met…
• 2025 IOE Department Faculty Award, College of Engineering, U…
• 2024 MLK Spirit Award for Community Building & Impact, Colle…
• 2024 INFORMS Moving Spirit Award for Forums, Institute for O…

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