Iterative minimization of quadratic functional
Copyright policy )Iterative minimization of quadratic functional
The author considers the gradient algorithm for minimizing convex continuous quadratic functionals in Hilbert spaces. He shows that the algorithm with fixed stepsize converges whenever the stepsize is small enough, and motivates his result by some filtering problems that occur in the theory of discrete time systems.
- The University of Texas at Austin United States
Hilbert spaces, quadratic optimization, gradient algorithm, Numerical mathematical programming methods, Least squares and related methods for stochastic control systems, contraction, Quadratic programming, filtering, discrete time systems
Hilbert spaces, quadratic optimization, gradient algorithm, Numerical mathematical programming methods, Least squares and related methods for stochastic control systems, contraction, Quadratic programming, filtering, discrete time systems
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