Abstract
We develop a polynomial-time procedure to handle a class of generalized fractional programming (GFP) problems with Toeplitz-Hermitian quadratics exploiting the linear matrix inequality (LMI) representation of the finite autocorrelation sequences cone, the spectral factorization theorem, and the Dinkelback's algorithm. For the special case of fractional quadratic programming (FQP) problems, we also provide a SemiDefinite programming (SDP) reformulation of the resulting non-convex optimization by means of the Charnes-Cooper transformation. Finally, we focus on an interesting radar signal processing application to assess the effectiveness of the devised optimization tool.
Published Version
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