Supporting global numerical optimization of rational functions by generic symbolic convexity tests
CASC'10 Proceedings of the 12th international conference on Computer algebra in scientific computing
Fast Calculation of Spectral Bounds for Hessian Matrices on Hyperrectangles
SIAM Journal on Matrix Analysis and Applications
Applying the canonical dual theory in optimal control problems
Journal of Global Optimization
Journal of Global Optimization
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We introduce a new method for the calculation of bounds on eigenvalues of Hessian matrices $\nabla^2 \varphi(x)$ of twice continuously differentiable functions $\varphi:U\subseteq\mathbb{R}^n\rightarrow \mathbb{R}$. The computational complexity of the new approach is shown to be of order ${\cal O}(n)\,N(\varphi)$ where $N(\varphi)$ is the number of operations necessary to evaluate the function $\varphi(x)$ at a point in its domain. This result is surprising, since the complexity of the calculation of the Hessian itself is of order ${\cal O}(n^2)\,N(\varphi)$ if the same method is used as in the proposed eigenvalue bounding approach. The favorable complexity of the new approach results because the eigenvalue bounds can be found without ever calculating the Hessian matrix.