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The canonical polyadic and rank-(Lr, Lr, 1) block term decomposition (CPD and BTD, respectively) are two closely related tensor decompositions. The CPD and, recently, BTD are important tools in psychometrics, chemometrics, neuroscience, and signal processing. We present a decomposition that generalizes these two and develop algorithms for its computation.(More)
Nonlinear optimization problems in complex variables are frequently encountered in applied mathematics and engineering applications such as control theory, signal processing, and electrical engineering. Optimization of these problems often requires a first-or second-order approximation of the objective function to generate a new step or descent direction.(More)
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