
doi: 10.1007/bf01580245
This paper presents a new globally convergent secant method for unconstrained optimization. The root rate of convergence of this algorithm is superlinear, between 1 and 2, decreasing with dimension of the problem. It is shown that on a class of problems, it is substantially more efficient than a number of other algorithms.
Convex programming, Numerical mathematical programming methods, Nonlinear programming
Convex programming, Numerical mathematical programming methods, Nonlinear programming
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