Primal-dual interior-point methods
Primal-dual interior-point methods
Convex Optimization
Constrained least squares design of 2-D FIR filters
IEEE Transactions on Signal Processing
Peak-constrained least-squares optimization
IEEE Transactions on Signal Processing
A fast matrix iterative technique for the WLS design of 2-D quadrantally symmetic FIR filters
Multidimensional Systems and Signal Processing
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This paper presents an online procedure that produces the smallest feasible size of two-dimensional FIR filters with prescribed magnitude error constraint. The procedure uses the mean square normalized error of constrained and unconstrained least-square filters to produce the initial and the subsequent sizes that converge to the smallest feasible one in a few iterations, where the constrained least-square filters are defined as the least-square filters satisfying the magnitude error constraint. The procedure finally returns a smallest size filter that satisfies the magnitude error constraint and has least total squared magnitude error. Design examples of diamond-shaped, rectangular, and elliptic filters are provided, and comparisons with an exhaustive search are given.