The new method exhibits a much more robust convergence behavior than existing iterative methods, such as frozen-nonlinearity iteration, Newton's method and damped Newton's method, and it can be used to find solutions when good initial guesses are unavailable.
The denser and more homogeneous the networks, the more robust the networks are against iterative path attacks.
In this work, the HLLC Riemann solver, which is much more robust, simpler and faster than iterative Riemann solvers, is extended to obtain interface conditions in sharp-interface methods for compressible multi-fluid flows.
We present PSPIKE+, as a family of parallel hybrid linear system solvers that are more robust than other available preconditioned iterative methods, and more scalable than parallel sparse direct solvers.
More robust clusters were produced by the iterative re-clustering procedure as shown by an increase in average pairwise identity and a decrease in variability among clusters at each round (Additional file 5: Figure S3).
The coefficient estimation for the ARMAX model can be achieved via an iterative search algorithm that minimizes a more robust quadratic prediction error criterion.
A number of numerical tests suggest that the proposed Riemann solver is suitable for general equations of state and has an accuracy comparable to iterative Riemann solvers, while being significantly more robust and efficient.
A more robust approach therefore is to merge this technique with the iterative majorization algorithm, such that the coordinates can be used as the initial estimates for the iteration.
Therefore, an iterative approach naturally allows to intensify the strength of the attack as more robust keypoints keep surviving; (2) changes in pixel values, in the attempt to remove a keypoint, may accidentally generate a new keypoint in the proximity.
