Efficient Computational Algorithm for Optimal Continuous Experimental Designs

TL;DR

This paper introduces an efficient algorithm for computing optimal continuous experimental designs in linear models, providing proofs of convergence and demonstrating superior performance through numerical examples.

Contribution

The paper presents a new computational algorithm for continuous optimal experimental design, with proofs of convergence for D-optimality and a proposed method for A-optimality.

Findings

The algorithm converges monotonically to the D-optimal design.

Numerical examples show the algorithm's effectiveness and efficiency.

The proposed methods outperform existing approaches in computational speed.

Abstract

A simple yet efficient computational algorithm for computing the continuous optimal experimental design for linear models is proposed. An alternative proof the monotonic convergence for $D$-optimal criterion on continuous design spaces are provided. We further show that the proposed algorithm converges to the $D$-optimal design. We also provide an algorithm for the $A$-optimality and conjecture that the algorithm convergence monotonically on continuous design spaces. Different numerical examples are used to demonstrated the usefulness and performance of the proposed algorithms.