Applicable Analysis· 2026Q2
A new notion of Tykhonov well-posedness for optimization problems
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- Q2SCImago
- 2026year
Short summary
A new definition of Tykhonov well-posedness is introduced, focusing on convergence along specific directions rather than the entire space, allowing previously ill-posed optimization problems to be well-posed under directional constraints.
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Key points
- Introduces Tykhonov well-posedness with respect to a set of directions for optimization.
- Generalizes classical Tykhonov well-posedness by focusing on directional convergence.
- Characterizes this new well-posedness using level set diameters and admissible functions.
- Demonstrates that problems not well-posed classically can be well-posed directionally.
- Enables reliable convergence of numerical methods to directional minimizers.
AI-generated from the title and abstract; the full text is not read.
Abstract
Building upon the minimal time function, we propose and study a novel notion of Tykhonov well-posedness with respect to a set of directions for optimization problems. This concept generalizes the classical Tykhonov well-posedness by focusing on existence, stability, and convergence along specific directions, rather than over the entire space. We first establish several characterizations of Tykhonov well-posedness with respect to a set of directions, formulated in terms of the diameter of level sets and admissible functions. We then investigate relationships between these level sets and admissible functions. To highlight the advantages of the proposed framework, we present several illustrative examples. In particular, we show that by selecting a suitable set of directions, optimization problems that are not well-posed in the classical sense may still be Tykhonov well-posed with respect to those directions. This viewpoint not only broadens the theoretical landscape of well-posedness but also has practical implications, as it allows numerical methods to be effectively adapted so that the generated sequences converge reliably to the desired directional minimizers.
The authors' abstract, as published at the source. Applicable Analysis, 2026 · DOI ↗
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Field: Computational Theory and Mathematics
Computational Theory and MathematicsComputer Science