71 citations · 368 across the 49 of their papers we have counts for
9 papers · 1 filter
Beyond IGO-Flow: Toward Convergence Analysis of IGO in Continuous Spaces
Ryosuke Kimura, Youhei Akimoto
Information-Geometric Optimization (IGO) provides a unified framework for black-box optimization by interpreting the adaptation of a search distribution as a natural gradient updat…
Simultaneous optimization of control gains and reference filter coefficients for trajectory tracking control
Amane Sakanashi, Rin Suyama, Atsuo Maki +1
Research on vessel automation and autonomy is currently being conducted by various countries and institutions. Safe and accurate ship control algorithms are crucial to realize auto…
Convergence rate of the (1+1)-evolution strategy on locally strongly convex functions with lipschitz continuous gradient
Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma +1
Evolution strategy (ES) is one of the promising classes of algorithms for black-box continuous optimization. Despite its broad successes in applications, theoretical analysis on th…
A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization
Ryoji Tanabe, Youhei Akimoto, Ken Kobayashi +3
This paper proposes a two-phase framework with a Bézier simplex-based interpolation method (TPB) for computationally expensive multi-objective optimization. The first phase in TPB…
Optimization on Planning of Trajectory and Control of Autonomous Berthing and Unberthing for the Realistic Port Geometry
Yoshiki Miyauchi, Ryohei Sawada, Youhei Akimoto +2
To realize autonomous shipping, autonomous berthing and unberthing are some of the technical challenges. In the past, numerous research have been done on the optimization of trajec…
Saddle Point Optimization with Approximate Minimization Oracle and its Application to Robust Berthing Control
Youhei Akimoto, Yoshiki Miyauchi, Atsuo Maki
We propose an approach to saddle point optimization relying only on oracles that solve minimization problems approximately. We analyze its convergence property on a strongly convex…
Saddle Point Optimization with Approximate Minimization Oracle
Youhei Akimoto
A major approach to saddle point optimization is a gradient based approach as is popularized by generative adversarial networks (GANs). In contrast, we analy…
An ODE Method to Prove the Geometric Convergence of Adaptive Stochastic Algorithms
Youhei Akimoto, Anne Auger, Nikolaus Hansen
We consider stochastic algorithms derived from methods for solving deterministic optimization problems, especially comparison-based algorithms derived from stochastic approximation…
Quality Gain Analysis of the Weighted Recombination Evolution Strategy on General Convex Quadratic Functions
Youhei Akimoto, Anne Auger, Nikolaus Hansen
Quality gain is the expected relative improvement of the function value in a single step of a search algorithm. Quality gain analysis reveals the dependencies of the quality gain o…