Semidefinite Programming is a matrix-form generalisation of linear programming, and is typically tackled using Interior Point Methods. These methods are of iterative nature and at each step, a matrix inversion needs to be performed. For small or sparse matrices, direct methods like sparse Cholesky factorisation are used. For dense matrices of larger size, like the ones that arise in convex...
In the context of high performance computing, new architectures, becoming more and more parallel, offer higher floating-point computing power. Thus, the size of the problems considered (and with it, the number of operations) increases, becoming a possible cause for increased uncertainty. As such, estimating the reliability of a result at a reasonable cost is of major importance for numerical...
This talk presents a summer student project that explored the numerical stability of MadGraph5 using CADNA. It focuses on how CADNA’s warning system and its ability to quantify floating-point precision were used to assess whether MadGraph5 can operate reliably with single-precision floating-point numbers.
