Convex Combination of Constraint Vectors for Set-membership Affine Projection Algorithms Article Swipe

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Constraint (computer-aided design) Algorithm Set (abstract data type) Regular polygon Affine transformation Convex set Projection (relational algebra) Computer science Mathematics Mathematical optimization Convex optimization Pure mathematics Geometry Programming language

Tadeu N. Ferreira , Wallace A. Martins , Markus V. S. Lima , Paulo S. R. Diniz ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.1109/icassp.2019.8682305 · OA: W2939307283

Set-membership affine projection (SM-AP) adaptive filters have been increasingly employed in the context of online data-selective learning. A key aspect for their good performance in terms of both convergence speed and steady-state mean-squared error is the choice of the so-called constraint vector. Optimal constraint vectors were recently proposed relying on convex optimization tools, which might sometimes lead to prohibitive computational burden. This paper proposes a convex combination of simpler constraint vectors whose performance approaches the optimal solution closely, utilizing much fewer computations. Some illustrative examples confirm that the sub-optimal solution follows the accomplishments of the optimal one.