This is where you will find the full catalogue of NAG published blog posts, from technical information to thought leadership.
Second-order cone programming (SOCP) is convex optimization which extends linear programming (LP) with second-order (Lorentz or the ice cream) cones.
Tags: NAG Library, Portfolio Optimization, SOCP
Designed from the outset for modern hardware, the nZetta Toolkit achieves a
10x performance improvement over reference 2D-PDE calculations on real finance problems.
Tags: Derivatives, nZetta Toolkit, PDE, Pricing, Quant Finance
With Monte Carlo, it is crucial to leverage the full potential of hardware parallelism to achieve optimal performance and accuracy.
Tags: Derivatives, Monte Carlo, nZetta Toolkit, Pricing
Trading Desks can hedge auspiciously and gain a competitive edge in the market as our mathematical algorithm technology, dco/c++, gives them rich, cheap and accurate intra-day risk. This fast and accurate risk data at lower cost means more profits for traders and for the business.
Tags: Automatic Differentiation, Finance, Risk Calculation
An approximate correlation matrix is one that is not positive semidefinite.
In this document we consider an application from finance.
Tags: Correlation Matrices, NAG Library
NAG has developed a CVA demonstration code to show how the NAG Library and the Algorithmic Differentiation (AD) tool dco/c++ can be combined with Origami to solve large scale CVA computations.
Tags: Cloud, CVA
An important problem in finance is to compute the implied volatility. Typically volatilities are computed for large vectors of input data.
Tags: Chebyshev Interpolation
Calculating XVA in a timely manner poses a big performance challenge
for financial institutions.
Tags: Adjoints, Automatic Differentiation, CVA
GS2  is an open source gyrokinetic simulation code used to study turbulence in plasma, one application is for fusion experiments. It is a gyrokinetic flux tube initial value and eigenvalue solv-er and is written in Fortran and parallelised with MPI.
This poster describes work performed on OpenFOAM focussing on
performance as well as OpenFOAM in the Cloud.
Tags: HPC, OpenFOAM
Adjoints are sophisticated numerical techniques for computing a large
number of gradients quickly. To compute an adjoint, your computer
program must be run backwards.
Tags: Adjoints, Automatic Differentiation, GPU
The C++ language is so complex that no AD compilers can handle it.
To get an adjoint, we must write it by hand or use an operator overloading
Tags: Adjoints, Automatic Differentiation, Tape Free