Recent News

January 22, 2014

Optimal Portfolio Allocation

The problem of optimal portfolio allocation, in its simplest form, asks the question of how to allocate a given amount of wealth across a fixed universe of investments to achieve a minimum-risk goal-expected return.  The known quantities are the potential field of investments, their performance history, and the goal rate of return; The unknown is the wealth allocation across the investments. [...]

December 16, 2013

Chebyshev Filters with NMath

There are three classes of widely used IIR (recursive) filters in signal processing: Butterworth, Chebyshev, and elliptical. In this article I will give a short introduction to the Chebyshev filter, present an efficient O(n log n) code implementation based on the FFT, and finish with a usage example. [...]

November 04, 2013

New NMath Video Series

We are proud to announce a series of tutorial videos on how to use CenterSpace's NMath products. We are starting, naturally, with Getting Started with NMath. The videos will be available on our new youtube channel, or you can just watch the first video here. Please let us know which topics you want us to cover. Email

October 30, 2013

IIR Filtering with Butterworth Filters

There are three classes of widely used IIR (recursive) filters in signal processing: Butterworth, Chebyshev, and elliptical. In this article I will discuss the Butterworth filter and provide example code implementing and using the filter.  Butterworth filters are desirable for their ease of implementation, good phase response, and their smooth monotonic frequency [...]

October 16, 2013

Smooth Cubic Splines

Smooth cubic splines embody a curve fitting technique which blends the ideas of cubic splines and curvature minimization to create an effective data modeling tool for noisy data. Traditional cubic splines represent the tabulated data as a piece-wise continuous curve which passes through each value in the data table. If we need to fit a spline to some non-uniformly sampled noisy data, the results can be rather unsatisfying because every data point is visited by the spline [...]

Announcing NMath Premium

Nvidia CenterSpace Software is pleased to announce the general availability of the Premium Editions of NMath 5.3 and NMath Stats 3.6. NMath Premium leverages the power of NVIDIA’s CUDA™ architecture for GPU-accelerated mathematics in .NET. CenterSpace has partnered with leading GPU computing experts to bring GPU-accelerated mathematics to the .NET platform for the first time.

NMath .NET Math and Statistics

The NMath .NET math and statistics libraries from CenterSpace Software provides building blocks for financial, engineering, and scientific applications on the Microsoft .NET platform.

Choose NMath. Written by experts, trusted by industry leaders.
Our developers are experts at object-oriented numerics, producing high-performance, rock solid components, with state of the art object-oriented .NET interfaces. NMath has satisfied customers in over 45 countries around the world, and is proven in production everyday inside Fortune 500 companies.

Increase your productivity and lower your development costs by using NMath to jumpstart your .NET numerical applications.
Our libraries are extensively tested, and include professional documentation, code examples, and technical support.

Licensing that works.
We have a simple royalty-free licensing model. Components are licensed per developer seat. There are no runtime or distribution fees for products developed that make use of NMath libraries.

The numerical tools you need, now and in the future.
NMath features include:

  • matrix and vector classes
  • random number generators
  • Fast Fourier Transforms (FFTs)
  • numerical integration
  • linear programming
  • linear regression
  • curve and surface fitting
  • optimization
  • hypothesis tests
  • analysis of variance (ANOVA)
  • probability distributions
  • principal component analysis
  • cluster analysis

NMath is built on the Intel® Math Kernel Library (MKL), which contains highly-optimized, extensively-threaded versions of the public domain computing packages known as the BLAS (Basic Linear Algebra Subroutines) and LAPACK (Linear Algebra PACKage).

All NMath routines are callable from any .NET language, including C#, Visual Basic.NET, and F#.

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