Beyond IGO-Flow: Toward Convergence Analysis of IGO in Continuous Spaces
Information-Geometric Optimization (IGO) provides a unified framework for black-box optimization by interpreting the adaptation of a search distribution as a natural gradient update. Despite its conceptual importance, the convergence theory of IGO remains limited: most existing results concern continuous-time idealizations such as the IGO flow, rather than discrete-time updates with non-infinitesimal learning rates. In this paper, we study discrete-time IGO in continuous spaces, formulated as natural gradient updates in the expectation-parameter coordinates of an exponential family. In particular, we analyze IGO over the multivariate Gaussian family on strongly convex quadratic objective ...
| Authors | Ryosuke Kimura, Youhei Akimoto |
|---|---|
| Primary category | math.OC |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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