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Linesearch Newton-CG methods for convex optimization with noise
Stefania Bellavia, Eugenio Fabrizi, Benedetta Morini
This paper studies the numerical solution of strictly convex unconstrained optimization problems by linesearch Newton-CG methods. We focus on methods employing inexact evaluations…
The Impact of Noise on Evaluation Complexity: The Deterministic Trust-Region Case
Stefania Bellavia, Gianmarco Gurioli, Benedetta Morini +1
Intrinsic noise in objective function and derivatives evaluations may cause premature termination of optimization algorithms. Evaluation complexity bounds taking this situation int…
Adaptive Regularization for Nonconvex Optimization Using Inexact Function Values and Randomly Perturbed Derivatives
S. Bellavia, G. Gurioli, B. Morini +1
A regularization algorithm allowing random noise in derivatives and inexact function values is proposed for computing approximate local critical points of any order for smooth unco…
Inexact restoration with subsampled trust-region methods for finite-sum minimization
Stefania Bellavia, Natasa Krejic, Benedetta Morini
Convex and nonconvex finite-sum minimization arises in many scientific computing and machine learning applications. Recently, first-order and second-order methods where objective f…
Adaptive Regularization Algorithms with Inexact Evaluations for Nonconvex Optimization
S. Bellavia, G. Gurioli, B. Morini +1
A regularization algorithm using inexact function values and inexact derivatives is proposed and its evaluation complexity analyzed. This algorithm is applicable to unconstrained p…
Adaptive Cubic Regularization Methods with Dynamic Inexact Hessian Information and Applications to Finite-Sum Minimization
Stefania Bellavia, Gianmarco Gurioli, Benedetta Morini
We consider the Adaptive Regularization with Cubics approach for solving nonconvex optimization problems and propose a new variant based on inexact Hessian information chosen dynam…