1 / 15

MCSL Monte Carlo simulation language

MCSL Monte Carlo simulation language. Diego Garcia Eita Shuto Yunling Wang Chong Zhai. Outline of Presentation. Introduction of language Language tutorial and examples Architectural design and implementation Summary and lessons learned. Monte Carlo methods.

lloyd
Télécharger la présentation

MCSL Monte Carlo simulation language

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MCSLMonte Carlo simulation language Diego Garcia Eita Shuto Yunling Wang Chong Zhai

  2. Outline of Presentation • Introduction of language • Language tutorial and examples • Architectural design and implementation • Summary and lessons learned

  3. 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

  4. 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

  5. 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)

  6. 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.

  7. Example One Calculation of “π” =3.14159265358979323846…

  8. Sample code for Calculate Pi

  9. Example 2-Pollard'srho algorithm

  10. Sample code for Factorization

  11. Language Implementation

  12. Architectural Design andImplementation

  13. Testing Cases

  14. Summary and lessons learned • Teamwork and effective project management • SVN (Subversion) on Googlecode • Incremental Development Approach • 1 • 2 • 3 • 4

  15. Thank You! • MCSL Team • Columbia University • Dec 19th, 2008

More Related