Convex optimization problems involving finite autocorrelation sequences |
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Authors: | Brien Alkire Lieven Vandenberghe |
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Institution: | (1) Department of Electrical Engineering, University of California, Los Angeles, e-mail: brien@alkires.com, e-mail: vandenbe@ee.ucla.edu, US |
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Abstract: | We discuss convex optimization problems in which some of the variables are constrained to be finite autocorrelation sequences.
Problems of this form arise in signal processing and communications, and we describe applications in filter design and system
identification. Autocorrelation constraints in optimization problems are often approximated by sampling the corresponding
power spectral density, which results in a set of linear inequalities. They can also be cast as linear matrix inequalities
via the Kalman-Yakubovich-Popov lemma. The linear matrix inequality formulation is exact, and results in convex optimization
problems that can be solved using interior-point methods for semidefinite programming. However, it has an important drawback:
to represent an autocorrelation sequence of length $n$, it requires the introduction of a large number ($n(n+1)/2$) of auxiliary
variables. This results in a high computational cost when general-purpose semidefinite programming solvers are used. We present
a more efficient implementation based on duality and on interior-point methods for convex problems with generalized linear
inequalities.
Received: August 20, 2001 / Accepted: July 16, 2002 Published online: September 27, 2002
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ID="★" This material is based upon work supported by the National Science Foundation under Grant No. ECS-9733450. |
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