Andreas Binder
Johannes Kepler University of Linz
26 Papers
96 Citations
Andreas Binder is an academic researcher from Johannes Kepler University of Linz. The author has contributed to research in topics: Continuous casting & Interest rate. The author has an hindex of 8, co-authored 26 publications.
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Papers
On the Landweber iteration for nonlinear ill-posed problems
TL;DR: In this article, the convergence conditions and regularization properties of Landweber iteration applied to nonlinear ill-posed operator equations are reviewed and a number of examples are given to illustrate the pre-assumptions required for the inherent regularization of LWE.
37
Optimal cooling strategies in continuous casting of steel with variable casting speed
TL;DR: The problem of computing an appropriate cooling strategy is reformulated as a finite-dimensional nonlinear optimization problem with bounds on the variables which is solved with a Quasi-Newton method and an algorithm for solving the inverse problem numerically is developed.
16
•Book
Introduction to Quantitative Methods for Financial Markets
Hansjoerg Albrecher,Andreas Binder,Volkmar Lautscham,Philipp Mayer +3 more
- 27 Jun 2013
TL;DR: The No-Arbitrage principle and the Black-Scholes formula were used in this paper to model interest rates and the valuation of interest rate derivatives, and the Binomial option pricing model was used to calculate stock-price models.
15
A fast and stable Heston model calibration on the GPU
Michael Aichinger,Andreas Binder,Johannes Fürst,Christian Kletzmayr +3 more
- 31 Aug 2010
TL;DR: A Fourier cosine method is used for the evaluation of the objective function, and the local/global optimization scheme is carried out on parallel architectures, resulting in a trustworthy optimum for the Heston model.
13
•Book
A workout in computational finance
Andreas Binder,Michael Aichinger +1 more
- 01 Jan 2013
TL;DR: Methods covered include PDE/PIDE using finite differences or finite elements, fast and stable solvers for sparse grid systems, stabilization and regularization techniques for inverse problems resulting from the calibration of financial models to market data, Monte Carlo and Quasi Monte Carlo techniques for simulating high dimensional systems, and local and global optimization tools to solve the minimization problem.