"We are currently working on the derivation of the so-called 'topological derivative' for the same nonlinear optimization problem," says Peter Gangl.

Moreover, nonlinear optimization can be computationally expensive in terms of time and memory, so care must be taken when matching an algorithm to a problem. All optimization techniques in PROC NLP ...

The problem of estimating the time-dependent attrition coefficients that best fit a set of given strength histories is inherently a nonparametric inverse problem. In this paper we cast it into a ...

The maximum likelihood estimation involved generates a high-dimensional mixed-integer nonlinear optimization problem. A highly efficient solution strategy is tested, exploiting the separable structure ...

This approach transforms the model parameter identification problem into a multi-objective nonlinear optimization problem with complex constraints. It allows for the calculation and accuracy analysis ...

Moreover, nonlinear optimization can be computationally expensive in terms of time and memory, so you must be careful when matching an algorithm to a problem. All optimization techniques in PROC ...

HOLO's nonlinear quantum optimization algorithm technology not only achieves a breakthrough in computational performance but also significantly improves resource utilization efficiency.

Did you consider yourself a mathematician the last time you sat down to solve a Sudoku puzzle? It’s certainly a mentally ...