Fractional Programming for Kullback-Leibler Divergence in Hypothesis Testing (opens in new tab)

Maximizing the Kullback-Leibler divergence (KLD) is a fundamental problem in waveform design for active sensing and hypothesis testing, as it directly relates to the error exponent of detection probability. However, the associated optimization problem is highly nonconvex due to the intricate coupling of log-determinant and matrix trace terms. Existing solutions often suffer from high computational complexity, typically requiring matrix inver...