Efficient computational algorithms for approximate optimal designs

In this paper, we propose two simple yet efficient computational algorithms to obtain approximate optimal designs for multi-dimensional linear regression on a large variety of design spaces. We focus the commonly used criteria, D- and A-optimal criteria. For D-optimality, provide an alternative proof monotonic convergence D-optimal criterion algorithm design. further show that proposed converges then proves continuous under certain conditions. A-optimality, conjecture monotonicity algorithm. Numerical comparisons suggest perform well they are comparable or superior some existing algorithms.