An Analysis of the Directional-Modifier Adaptation Algorithm Based on Optimal Experimental Design

Academic Editor: Dominique Bonvin Received: 1 November 2016; Accepted: 15 December 2016; Published: 22 December 2016 Abstract: The modifier approach has been extensively explored and offers a theoretically-sound and practically-useful method to deploy real-time optimization. The recent directional-modifier adaptation algorithm offers a heuristic to tackle the modifier approach. The directional-modifier adaptation algorithm, supported by strong theoretical properties and the ease of deployment in practice, proposes a meaningful compromise between process optimality and quickly improving the quality of the estimation of the gradient of the process cost function. This paper proposes a novel view of the directional-modifier adaptation algorithm, as an approximation of the optimal trade-off between the underlying experimental design problem and the process optimization problem. It moreover suggests a minor modification in the tuning of the algorithm, so as to make it a more genuine approximation.