endmember spectra $$\label{eq:LMM}
  \Vpix{\nopix}^{(\textrm{LMM})} = \sum_{\nomat=1}^{\nbmat} \abond{\nomat}{\nopix}
  \Vmat{\nomat} + \Vnoise{\nopix} = \MATmat\Vabond{\nopix} +
  \Vnoise{\nopix}$$ where $\Vabond{\nopix}=\left[\abond{1}{\nopix},\dots,\abond{\nbmat}{\nopix}\right]^T$ denotes the proportions of the $\nbmat$ materials in the $\nopix$th pixel, $\MATmat=\left[\Vmat{1},\ldots,\Vmat{\nbmat}\right]$ is the endmember matrix and $\Vnoise{\nopix}$ stands for an additive residual term accounting for the measurement noise and modeling error. Since the mixing coefficients $\abond{1}{\nopix},\ldots,\abond{\nbmat}{\nopix}$ are expected to represent the actual spatial distribution of the materials in the $\nopix$th pixel, they are commonly subject to the following positivity and sum-to-one (or additivity) constraints $$\label{eq:constraints_LMM}
  \left\{
  \begin{array}{ll}
    \abond{\nomat}{\nopix}\geq 0, & \forall \nomat,\ \forall \nopix  \\
    \sum_{\nomat=1}^{\nbmat} \abond{\nomat}{\nopix}=1, & \forall \nopix . \\
  \end{array}
  \right.$$ This LMM has received a considerable attention in the image processing and remote sensing literature since it represents an acceptable first-order approximation of the physical processes involved in most of the scenes of interest [@Keshava2002]. Consequently, it has motivated a lot of research works that aim at developing efficient endmember extraction algorithms (EEA), designed to recover pure spectral signatures in the image, and inversion techniques to estimate the abundance coefficients for a given (estimated or *a priori* known) set of endmembers. Comprehensive overviews of these EEA and inversion methods can be found in [@Keshava2002; @Somers2011; @Bioucas2012jstars]. Specifically, two main 