First- and second-order high probability complexity bounds for trust-region methods with noisy oracles
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Publication:6608030
DOI10.1007/S10107-023-01999-5zbMATH Open1547.65056MaRDI QIDQ6608030
Katya Scheinberg, Albert S. Berahas, Liyuan Cao
Publication date: 19 September 2024
Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)
Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Derivative-free methods and methods using generalized derivatives (90C56)
Cites Work
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- Stochastic optimization using a trust-region method and random models
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- Recent advances in trust region algorithms
- Random gradient-free minimization of convex functions
- Cubic regularization of Newton method and its global performance
- On the Lagrange functions of quadratic models that are defined by interpolation*
- Convergence of Trust-Region Methods Based on Probabilistic Models
- Introduction to Derivative-Free Optimization
- Trust Region Methods
- Complexity and global rates of trust-region methods based on probabilistic models
- Derivative-Free Optimization of Noisy Functions via Quasi-Newton Methods
- High-Dimensional Probability
- Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise
- Benchmarking Derivative-Free Optimization Algorithms
- On the Global Convergence of Trust Region Algorithms Using Inexact Gradient Information
- A Stochastic Line Search Method with Expected Complexity Analysis
Related Items (2)
A non-monotone trust-region method with noisy oracles and additional sampling ⋮ A sequential quadratic programming method with high-probability complexity bounds for nonlinear equality-constrained stochastic optimization
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