arXiv:2505.02281v3 Announce Type: replace-cross Abstract: This paper explores the performance of a random Gaussian smoothing zeroth-order (ZO) scheme for minimising quasar-convex (QC) and strongly quasar-convex (SQC) functions in both unconstrained and constrained settings. For the unconstrained problem, we establish the ZO algorithm's convergence to a global minimum along with its complexity when applied to both QC and SQC functions. For the constrained problem, we introduce the new notion of proximal-quasar-convexity and prove analogous results to the unconstrained case. Specifically, we der
This academic paper, published in 2026 according to the date, presents theoretical advancements in minimisation algorithms relevant to machine learning and optimisation.
While foundational, this research is highly technical and specific to the mathematical underpinnings of AI, not immediately impacting strategic readers.
This paper refines theoretical understanding in optimisation, which could indirectly lead to more efficient AI algorithms in the long term, but does not represent a direct change in current AI capabilities or applications.
Improved theoretical understanding of optimisation for a specific class of functions.
Potential for slightly more efficient AI training or model deployment in specific research areas years down the line.
Very long-term and indirect contribution to the overall robustness and capability of advanced AI systems.
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Read at arXiv cs.AI