Analysis of the SQP Method for Hyperbolic PDE-Constrained Optimization in Acoustic Full Waveform Inversion

In this paper, the SQP method applied to a hyperbolic PDE-constrained optimization problem is considered. The model arises from the acoustic full waveform inversion in the time domain. The analysis is mainly challenging due to the involved hyperbolicity and second-order bilinear structure. This notorious character leads to an undesired effect of loss of regularity in the SQP method, calling for a substantial extension of developed parabolic techniques. We propose and analyze a novel strategy for the well-posedness and convergence analysis based on the use of a smooth-in-time initial condition, a tailored self-mapping operator, and a two-step estimation process along with Stampacchia's method for second-order wave equations. Our final theoretical result is the R-superlinear convergence of the SQP method.

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Publication Details

Journal
SIAM Journal on Control and Optimization
Published
2026-05-12
DOI
https://doi.org/10.1137/25m1722822
Primary Topic
Image and Signal Denoising Methods
Type
preprint
Field-Weighted Citation Impact
0.00

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preprint

Analysis of the SQP Method for Hyperbolic PDE-Constrained Optimization in Acoustic Full Waveform Inversion

Irwin Yousept, Luis Ammann
SIAM Journal on Control and Optimization
Image and Signal Denoising Methods
preprint

Analysis of the SQP Method for Hyperbolic PDE-Constrained Optimization in Acoustic Full Waveform Inversion

Irwin Yousept, Luis Ammann
preprint en

Abstract

In this paper, the SQP method applied to a hyperbolic PDE-constrained optimization problem is considered. The model arises from the acoustic full waveform inversion in the time domain. The analysis is mainly challenging due to the involved hyperbolicity and second-order bilinear structure. This notorious character leads to an undesired effect of loss of regularity in the SQP method, calling for a substantial extension of developed parabolic techniques. We propose and analyze a novel strategy for the well-posedness and convergence analysis based on the use of a smooth-in-time initial condition, a tailored self-mapping operator, and a two-step estimation process along with Stampacchia's method for second-order wave equations. Our final theoretical result is the R-superlinear convergence of the SQP method.

SIAM Journal on Control and OptimizationVol. 64(3)
University of Duisburg-Essen (DE)
Deutsche Forschungsgemeinschaft, Universidad de Oviedo, Julius-Maximilians-Universität Würzburg
Openalex Percentile: Top 100%
Image and Signal Denoising Methods
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