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MCSL Monte Carlo simulation languagePowerPoint Presentation

MCSL Monte Carlo simulation language

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Presentation Transcript

Outline of Presentation

- Introduction of language
- Language tutorial and examples
- Architectural design and implementation
- Summary and lessons learned

Monte Carlo methods

- a class of computational algorithms that rely on repeated random sampling to compute their results.
- Why interested with it?
- widely used
- good performance
- possibly the only
- efficient approach

Applications

- Physics
- high energy particle physics, quantum many-body problem, transportation theory

- Mathematics
- Integration, Optimization, Inverse problems, Computational mathematics

- Computer science
- Las Vegas algorithm, LURCH, Computer Go, General Game Playing
- Finance
- Option, instrument, portfolio or investment

Algorithm of MCSL

- Generation particular distributed psedu-random numbers (or low discrepancy sequence)
- Evaluate the function by sampling with these numbers
- Aggregate results! (weight might be counted, variational method or conditional acceptance might be considered)

Advantages

- Fast Random number algorithm (with good performance also)
- Other than iteration, consider all samples as a single vector
- Built-in Function to simplify aggregate process with different conditioning.

Example One Calculation of “π” =3.14159265358979323846…

Example 2-Pollard'srho algorithm

Architectural Design andImplementation

Summary and lessons learned

- Teamwork and effective project management
- SVN (Subversion) on Googlecode
- Incremental Development Approach
- 1
- 2
- 3
- 4

Thank You!

- MCSL Team
- Columbia University
- Dec 19th, 2008

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