Sample complexity analysis for adaptive optimization algorithms with stochastic oracles
Author:
Funder
NSF
Office of Naval Research
NSERC
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10107-024-02078-z.pdf
Reference24 articles.
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2. Paquette, C., Scheinberg, K.: A stochastic line search method with expected complexity analysis. SIAM J. Optim. 30(1), 349–376 (2020)
3. Berahas, A.S., Cao, L., Scheinberg, K.: Global convergence rate analysis of a generic line search algorithm with noise. SIAM J. Optim. 1489–1518 (2019)
4. Blanchet, J., Cartis, C., Menickelly, M., Scheinberg, K.: Convergence rate analysis of a stochastic trust-region method via supermartingales. INFORMS J. Optim. 1(2), 92–119 (2019)
5. Gratton, S., Royer, C.W., Vicente, L.N., Zhang, Z.: Complexity and global rates of trust-region methods based on probabilistic models. IMA J. Numer. Anal. 38(3), 1579–1597 (2018)
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