Gradient and diagonal Hessian approximations using quadratic interpolation models and aligned regular bases

Author:

Coope Ian D.,Tappenden RachaelORCID

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics

Reference27 articles.

1. Audet, C., Hare, W.: Derivative-free and blackbox optimization. Springer Series in Operations Research and Financial Engineering, Springer (2017)

2. Bandeira, A.S., Scheinberg, K., Vicente, L.N.: Computation of sparse low degree interpolating polynomials and their application to derivative-free optimization. Mathematical Programming Series B 134, 223–257 (2012)

3. Baydin, A.G., Pearlmutter, B.A., Radul, A.A., Siskind, J.M.: Automatic differentiation in machine learning: a survey. J. Mach. Learn. Res. 18, 1–43 (2018). Editor: Léon Bottou

4. Berahas, A.S., Cao, L., Choromanskiy, K., Scheinberg, K.: A theoretical and empirical comparison of gradient approximations in derivative-free optimization. Tech. rep., Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA, USA. arXiv:1905.01332v2 [math.OC] (2019)

5. Cocchi, G., Liuzzi, G., Papini, A., Sciandrone, M.: An implicit filtering algorithm for derivative-free multiobjective optimization with box constraints. Comput. Optim. Appl. 69(2), 267–296 (2018)

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