The Impact of Noise on Evaluation Complexity: The Deterministic Trust-Region Case

Stefania Bellavia (), Gianmarco Gurioli (), Benedetta Morini () and Philippe Louis Toint ()
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Stefania Bellavia: Università degli Studi di Firenze
Gianmarco Gurioli: Institute of Information Science and Technologies “A. Faedo” ISTI-CNR
Benedetta Morini: Università degli Studi di Firenze
Philippe Louis Toint: University of Namur

Journal of Optimization Theory and Applications, 2023, vol. 196, issue 2, No 13, 700-729

Abstract: Abstract Intrinsic noise in objective function and derivatives evaluations may cause premature termination of optimization algorithms. Evaluation complexity bounds taking this situation into account are presented in the framework of a deterministic trust-region method. The results show that the presence of intrinsic noise may dominate these bounds, in contrast with what is known for methods in which the inexactness in function and derivatives’ evaluations is fully controllable. Moreover, the new analysis provides estimates of the optimality level achievable, should noise cause early termination. Numerical experiments are reported that support the theory. The analysis finally sheds some light on the impact of inexact computer arithmetic on evaluation complexity.

Keywords: Noise; Evaluation complexity; Trust-region methods; Inexact functions and derivatives (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10957-022-02153-5

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