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A tool for the analysis of quasi-Newton methods with application to unconstrained minimization. (English) Zbl 0676.65061
The object of this paper is to present a very general result about the BFGS update formula and to introduce some tools that can be very useful in the convergence analysis of quasi-Newton methods of the form \(x_{k+1}=x_ k+\alpha_ kd_ k\) for the unconstrained optimization problem min f(x), \(x\in R^ n\) where \(\alpha_ k\) is the steplength, and \(d_ k\) is a search direction of the form \(d_ k=-B_ k^{-1}g_ k\). \(g_ k\) is the gradient of f at \(x_ k\) and the matrix \(B_ k\) is updated at every step by means of a quasi-Newton update formula: \(B_{k+1}=B_ k-(B_ ks_ ks^ T_ kB_ k)/(s^ T_ kB_ ks_ k)+(y_ ky^ T_ k)/(y^ T_ ks_ k)\), where \(y_ k=g_{k+1}-g_ k\) and \(s_ k=x_{k+1}-x_ k.\)
At first the authors show under very mild conditions a basic property of the BFGS update formula. The main result is the proof that the BFGS method with any of a wide variety of line searches is globally and superlinearly convergent on uniformly convex objective functions. Furthermore is shown that a backtracking line search satisfies the conditions for the given convergence results.
Reviewer: H.Benker

MSC:
65K05 Numerical mathematical programming methods
90C25 Convex programming
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