We consider iterative trust region algorithms for the unconstrained minimization of an objective function <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$F ( \underline{x})$</EquationSource> </InlineEquation>, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$\underline{x}\in \mathcal{R}^{n}$</EquationSource> </InlineEquation>, when F is differentiable but no derivatives are available, and when each model of F is a linear or a quadratic polynomial. The models interpolate F at n+1 points, which defines them uniquely when they are linear polynomials. In the quadratic case, second derivatives of the models are derived from information from previous iterations, but there are so few data that typically only the magnitudes of second derivative estimates are correct. Nevertheless, numerical results show that much faster convergence is achieved when quadratic models are employed instead of linear ones. Just one new value of F is calculated on each iteration. Changes to the variables are either trust region steps or are designed to maintain suitable volumes and diameters of the convex hulls of the interpolation points. It is proved that, if F is bounded below, if ∇<Superscript>2</Superscript> F is also bounded, and if the number of iterations is infinite, then the sequence of gradients <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$\underline{\nabla}F ( \underline{x}_{\,k} )$</EquationSource> </InlineEquation>, k=1,2,3,…, converges to zero, where <InlineEquation ID="IEq4"> <EquationSource Format="TEX">$\underline{x}_{\,k}$</EquationSource> </InlineEquation> is the centre of the trust region of the k-th iteration. Copyright Springer Science+Business Media, LLC 2012
