Projects tagged ‘numerics’

DUNE, the Distributed and Unified Numerics Environment is a modular toolbox for solving partial differential equations (PDEs) with grid-based methods. It supports the easy implementation of methods ... [More] like Finite Elements (FE), Finite Volumes (FV), and also Finite Differences (FD). [Less]

dnAnalytics is a numerical library for the .NET Framework licensed under the Microsoft Public License. The library is written in C# and is available as a fully managed library, or as a native version ... [More] that uses Intel® Math Kernel Library (MKL). The native version of dnAnalytics provides significantly better performance when working with large sets of data. dnAnalytics is compatible with .NET 2.0 or later, and Mono. The managed version will run on a Windows XP or newer, and any platform that supports Mono. The native version supports 32bit and 64bit versions of Windows XP or newer, and 32bit and 64bit versions of Linux. [Less]

A purely functional interface to basic linear algebra computations and other numerical routines, internally implemented using GSL, BLAS and LAPACK.

ASCEND is an interactive environment for modeling, debugging, and solving large systems of nonlinear algebraic or differential equations. Its interactive design allows users to inspect and solve ... [More] very difficult nonlinear systems. It includes plotting abilities and is scriptable. [Less]

Provides a class for non-negative numbers, a wrapper which can turn any ordered numeric type into a member of that class, and a lazy number type for non-negative numbers (a generalization of Peano ... [More] numbers). This library is used by the event-list package [Less]

Eigentools presents a C++ package to wrap the messiness associated with solving large scale sparse eigenproblems.

Not interesting. Personal numeric method implementations through Java - gwt.

EXISTING PROBLEM: NUMERICAL INTEGRATION Definite integrals arise in many different areas and the Fundamental Theorem of Calculus is a powerful tool for evaluating definite integrals. However, it ... [More] cannot always be applied. There are some functions which do not have an antiderivative which can be expressed in terms of familiar functions such as polynomials, exponentials and trigonometric functions. One such example is E(-X2). Of course, this is an important function since it is the probability density function for the normal distribution. Moreover, we sometimes only have information about a function by making observations at a certain number of points. In that case, we do not have a nice formula for the function we are integrating, but only some data points. One of the current solution to the above problem is the Trapezoidal Rule. EXISTING SOLUTION: THE TRAPEZOIDAL RULE The trapezoidal rule uses trapezoids instead of rectangles to approximate the definite interval over a closed bounded interval. By using points on the graph of the function determined by a uniform width partition of the interval the upper boundary of the trapezoid is formed. Of course the more subintervals, (or said another way: the more trapezoids) the more accuracy of the estimation. And here lies the biggest challenge in the implementation of the Trapezoidal Rule - the sheer computational complexity involved - particularly when high levels of accuracy are required. SOLUTION PROPOSED USING CUDA / PROJECT OBJECTIVE We have proposed a parallel algorithm for the Trapezoidal Rule, which exploits the poer of CUDA. Running 4 blocks of 256 threads each, per call - subject to a maximum limit of 2^27 calls (after this the function starts making approximations). CODE BRIEF On execution, the user is asked to choose a mode for computation - Quick, Standard or Extended - depending on which the relevant function is called. In the Quick or the Default Mode, the Integration is performed over from 0 to 1. The accuracy is two decimal places. In the Standard and Extended modes, the user gets to choose one out of the 5 common types of functions: Inverse, Logarithmic, Algebraic, Trigonometric and Exponential. The accuracy is three decimal places in Standard, while it increases to 6 decimal places in Extended Mode. In addition, the Extended Mode also allows the user to control the main kernel function. He can specify the Depth of Recursion at which the function should start making serial calls, as well as the Depth of recursion at which it should quit. [Less]

Solving boundary integral equations numerically requires computing a dense linear system of equations A x = b. By exploiting the smoothness of the kernel function, computation of the matrix entries will scale linearly instead of quadratically.

Dumping ground for random pieces of numerical codes.