Studying Convergence of Gradient Algorithms Via Optimal Experimental Design Theory
We study the family of gradient algorithms for solving quadratic optimization problems, where the step-length γk is chosen according to a particular procedure. To carry out the study, we re-write the algorithms in a normalized form and make a connection with the theory of optimum experimental design. We provide the results of a numerical study which shows that some of the proposed algorithms are extremely efficient.
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