Efficient Online Inverse Optimization with $O(d)$ Regret

2026-09-11

AI Summary

本文提出了一种在线逆线性优化的确定性算法，通过变量度量框架和自归一化秩-1更新方法，在每轮时间内达到O(d)遗憾界。

Abstract

We give a deterministic algorithm for online inverse linear optimization with regret $O(d)$, uniform in the horizon and $O(d^{2})$ time per round. A bound of this order was obtained recently by Dewasurendra, settling a question of Gollapudi et al.\ and of Oki and Sakaue, but by an improper rule that enumerates covers at every scale and costs $T^{Θ(d)}$ a round; ours is the first efficient such bound and the first proper one. We build on the variable-metric framework of Sakaue et al., adding a self-normalized rank-one update, and we replace the $\log\det$ potential by the trace power $\tr(H^{-1/2})$, which is bounded outright and removes the $\ln T$. The bound also holds against an expert that does not optimize, and we give corruption-robust and rank-adaptive variants, and an application to convex minimization.

Problem

• online inverse linear optimization
• regret bound
• deterministic algorithm

Innovation

• online inverse linear optimization
• self-normalized rank-one update
• trace power