Optimized Stochastic Approach for Integral Equations

• Autorzy: Todorov Venelin , Fidanova Stefka , Dimov Ivan , Georgieva Rayna

Identyfikatory

DOI

10.15439/2021F54

• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

An optimized Monte Carlo approach (OPTIMIZED MC) for a Fredholm integral equations of the second kind is presented and discussed in the present paper. Numerical examples and results are discussed and MC algorithms with various initial and transition probabilities are compared.

Słowa kluczowe

EN

Fredholm integral equations; Monte Carlo methods; optimized stochastic approach

PL

równanie całkowe Fredholma; metody Monte Carlo; zoptymalizowane podejście stochastyczne

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 25
• Strony: 239--242
• Opis fizyczny: Bibliogr. 7 poz., wz., tab., wykr.

Twórcy

autor

Todorov Venelin

• Bulgarian Academy of Sciences, Institute of Mathematics and Informatics ul. G. Bonchev 8, 1113 Sofia, Bulgaria
• Bulgarian Academy of Sciences, Institute of Information and CommunicationTechnologies ul. G. Bonchev 25 A, 1113 Sofia, Bulgaria

autor

Fidanova Stefka

• Bulgarian Academy of Sciences, Institute of Information and CommunicationTechnologies ul. G. Bonchev 25 A, 1113 Sofia, Bulgaria

autor

Dimov Ivan

• Bulgarian Academy of Sciences, Institute of Information and CommunicationTechnologies ul. G. Bonchev 25 A, 1113 Sofia, Bulgaria

autor

Georgieva Rayna

• Bulgarian Academy of Sciences, Institute of Information and CommunicationTechnologies ul. G. Bonchev 25 A, 1113 Sofia, Bulgaria

Bibliografia

• 1. I. Dimov, Monte Carlo Methods for Applied Scientists, New Jersey, London, Singapore, World Scientific, 2008, 291p.
• 2. R. Georgieva, PhD Thesis: Computational complexity of Monte Carlo algorithms for multidimensional integrals and integral equations, Sofia, 2003
• 3. I. Dimov, E. Atanassov, What Monte Carlo models can do and cannot do efficiently?, Applied Mathematical Modelling 32(2007) 1477–1500.
• 4. J.H. Curtiss. Monte Carlo Methods for The Iteration of Linear Operators. J. Math. Phys., 32 209–232, (1954).
• 5. A. Doucet, A.M. Johansen, V.B. Tadic. On solving integral equations using Markov chain Monte Carlo methods. Applied Mathematics and Computations, 216 2869–2880, (2010).
• 6. I. Sobol. Numerical methods Monte Carlo. Nauka, Moscow, 1973.
• 7. S.L. Zaharieva, I. Radoslavov Georgiev, V.A. Mutkov and Y. Branimirov Neikov, "Arima Approach For Forecasting Temperature In A Residential Premises Part 2, "2021 20th International Symposium Infoteh-jahorina (infoteh), 2021, pp.1-5.

Uwagi

1. Track 1: Artificial Intelligence in Applications

2. Session: 14th International Workshop on Computational Optimization

3. Short Paper