arXiv:2511.22283v2 Announce Type: replace Abstract: Online mirror descent (OMD) is a fundamental algorithmic paradigm that underlies many algorithms in optimization, machine learning and sequential decision-making. The OMD iterates are defined as solutions to optimization subproblems which, oftentimes, can be solved only approximately, leading to an inexact version of the algorithm. Nonetheless, existing OMD analyses typically assume an idealized error free setting, thereby limiting our understanding of performance guarantees that should be expected in practice. In this work we initiate a syst
This research provides a more realistic understanding of Online Mirror Descent (OMD) performance by accounting for practical approximations, moving beyond idealized theoretical assumptions.
Improved understanding of OMD's limitations and practical performance is crucial for developing more robust and reliable AI algorithms, impacting fields from optimization to sequential decision-making.
The focus shifts from idealized OMD theory to its practical, 'inexact' implementation, influencing how researchers and practitioners design and evaluate machine learning systems.
- · AI researchers focusing on practical algorithm deployment
- · Machine learning engineers dealing with real-world computational constraints
- · Developers of sequential decision-making systems
- · Theorists operating solely on idealized computational models
- · Organizations relying on overly optimistic performance projections for OMD-based
More accurate performance predictions for OMD-based algorithms will emerge, bridging the gap between theory and practice.
This improved understanding could lead to the development of new, more resilient algorithms that explicitly account for approximation errors.
These more robust algorithms could enable the deployment of AI systems in more sensitive or computationally constrained environments, where reliability is paramount.
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Read at arXiv cs.LG