A flexible gradient tracking algorithmic framework for decentralized optimization.
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- Computational Optimization & Applications, 2025, v. 91, n. 3, p. 1073, doi. 10.1007/s10589-025-00685-w
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- Article
Works matching AU Berahas, Albert S.
Results: 10
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An investigation of Newton-Sketch and subsampled Newton methods.
- Published in:
- Optimization Methods & Software, 2020, v. 35, n. 4, p. 661, doi. 10.1080/10556788.2020.1725751
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- Publication type:
- Article
3
A robust multi-batch L-BFGS method for machine learning.
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- Optimization Methods & Software, 2020, v. 35, n. 1, p. 191, doi. 10.1080/10556788.2019.1658107
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- Publication type:
- Article
4
A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear-Equality-Constrained Optimization with Rank-Deficient Jacobians.
- Published in:
- Mathematics of Operations Research, 2024, v. 49, n. 4, p. 2212, doi. 10.1287/moor.2021.0154
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- Article
5
Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized Optimization.
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- Journal of Optimization Theory & Applications, 2024, v. 203, n. 3, p. 2954, doi. 10.1007/s10957-024-02554-8
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- Publication type:
- Article
6
Multiblock Parameter Calibration in Computer Models.
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- INFORMS Journal on Data Science, 2023, v. 2, n. 2, p. 116, doi. 10.1287/ijds.2023.0029
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- Article
7
First- and second-order high probability complexity bounds for trust-region methods with noisy oracles.
- Published in:
- Mathematical Programming, 2024, v. 207, n. 1/2, p. 55, doi. 10.1007/s10107-023-01999-5
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- Publication type:
- Article
8
Limited-memory BFGS with displacement aggregation.
- Published in:
- Mathematical Programming, 2022, v. 194, n. 1/2, p. 121, doi. 10.1007/s10107-021-01621-6
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- Publication type:
- Article
9
Accelerating stochastic sequential quadratic programming for equality constrained optimization using predictive variance reduction.
- Published in:
- Computational Optimization & Applications, 2023, v. 86, n. 1, p. 79, doi. 10.1007/s10589-023-00483-2
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- Publication type:
- Article
10
A Theoretical and Empirical Comparison of Gradient Approximations in Derivative-Free Optimization.
- Published in:
- Foundations of Computational Mathematics, 2022, v. 22, n. 2, p. 507, doi. 10.1007/s10208-021-09513-z
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- Article