6 papers
Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input
So Nakashima, Tetsuya J. Kobayashi
We investigate Optimization under Input Uncertainty (OIU), in which the input to the objective function, rather than the objective function itself, is subject to uncertainty. OIU a…
Convex Analysis of Relaxation Dynamics in Chemical Reaction Networks and Generalized Gradient Flows
Keisuke Sugie, Dimitri Loutchko, Tetsuya J. Kobayashi
We obtain bounds on the Kullback--Leibler divergence to equilibrium for mass-action chemical reaction networks (CRNs) with equilibrium. The associated decay rates are characterized…
Information geometry of perturbed gradient flow systems on hypergraphs: A perspective towards nonequilibrium physics
Dimitri Loutchko, Keisuke Sugie, Tetsuya J Kobayashi
This article serves to concisely review the link between gradient flow systems on hypergraphs and information geometry which has been established within the last five years. Gradie…
Information geometry of chemical reaction networks: Cramer-Rao bound and absolute sensitivity revisited
Dimitri Loutchko, Yuki Sughiyama, Tetsuya J. Kobayashi
Information geometry is based on classical Legendre duality but allows to incorporate additional structure such as algebraic constraints and Bregman divergence functions. It is nat…
Cramer-Rao bound and absolute sensitivity in chemical reaction networks
Dimitri Loutchko, Yuki Sughiyama, Tetsuya J. Kobayashi
Chemical reaction networks (CRN) comprise an important class of models to understand biological functions such as cellular information processing, the robustness and control of met…
Optimality theory of stigmergic collective information processing by chemotactic cells
Masaki Kato, Tetsuya J. Kobayashi
Collective information processing is fundamental in various biological systems, where the cooperation of multiple cells results in complex functions beyond individual capabilities.…