Abstract
The problem of choosing an optimal signal set for non-Gaussian detection was reduced to a smooth inequality-constrained mini-max nonlinear programming problem by Gockenbach and Kearsley. Here, we consider the application of several optimization algorithms, both global and local, to this problem. The most promising results are obtained when special-purpose sequential quadratic programming algorithms are embedded into stochastic global algorithms.
Notes
†A major iteration is defined here as one solution of PES(ρ) and one update of ρ.