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Embeddable Numerical Analysis Functions for C/C++ Analytical Applications
The IMSL C Numerical Library provides advanced mathematical and statistical functionality that is embeddable in numerical applications written in C/C++.
Using PyIMSL, developers can write programs in Python that leverage algorithms in the IMSL C Library. PyIMSL is a collection of Python wrappers to the math and statistical algorithms in the IMSL C Numerical Library that is available free of charge to IMSL C Library users.
The IMSL C Library features:
- the world’s fastest and most robust dense Linear Programming Optimizer in a general math library.
IMSL C Library is faster....
IMSL C Library is more robust...
- a world-class forecasting package delivering:
- Advanced Neural Network data pre-conditioners, trainers, and forecasting engines
- Auto_ARIMA “expert system” automated forecasting algorithm
- Time-series data pre-conditioners
- Outlier detection
- Outlier classification
- Missing value estimation
- LAPACK
- Linear algebra, often used in high-performance computing – now easily accessible in C
- Mersenne Twister Random Number Generator
- Very high-quality random numbers
NEW!Parallelization of numerous algorithms using OpenMP
- Take advantage of multi-core and many-core hardware for improved performance. Numerous algorithms leverage OpenMP directives on supported environments to distribute calculations across available resources.
NEW!New function that solves the generalized Feynman-Kac PDE and Black-Scholes problems
Learn more about this algorithm in our White Papers:
 Integrating Feynman-Kac Equations Using Hermite Quintic Finite Elements – describes the method for solving the Feynman-Kac PDE- Solving Constrained Differential-Algebraic Systems Using Projections - describes specific software additions to the Differential Algebraic Equation solver code
NEW!New data mining functions including a Genetic Algorithm for optimization and Naïve Bayes for classification problems and text mining
- The IMSL Library Genetic Algorithm implementation supports the basic algorithm originally introduced in the 1970s with the most popular variations. This capability is achieved by supporting variations such as user defined population size and selection methods, random or user defined initial populations, any combination of four different data types: nominal, binary, integer and real, and user supplied fitness functions with or without additional function parameters. These many variations offer developers a level of flexibility in the IMSL Library Genetic Algorithm functions that is unmatched in alternative solutions.
- Naïve Bayes is a simple algorithm that is very fast. A Naïve Bayes classifier can be trained to classify patterns involving thousands of attributes and applied to thousands of patterns. As a result, Naïve Bayes is a preferred algorithm for text mining and other large classification problems.
NEW!New functions including:
- Kochanek-Bartels Cubic Splines
- Non-central chi-square, Non-central student’s T PDFs
NEW!Enhancements to existing algorithms, including:
- Improved algorithm for finding zeros of a function
- Faster normal random number generation
- Neural network classification capability
- Multiple options for selecting Auto_ARIMA models
The IMSL C Library is leveraged in many application areas including:
- Portfolio Optimization in financial services
- Risk Management in financial services
- Inventory management and demand forecasting
- Modeling and simulation in high performance computing
- Computational biology analysis and modeling
- ISVs embedding mathematical engines into their software offerings
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IMSL Numerical Libraries Overview and Benefits
IMSL Libraries – Analytics Areas of Functionalities
IMSL Libraries – Extensive Environment Support
IMSL Libraries:
IMSL Libraries for Forecasting and Data Mining
IMSL Libraries: Auto Arima and Neural Networks
IMSL Libraries for Finance
Documentation
White Papers
Application Areas
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800.222.4675
or email info@vni.com
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