Randomized Algorithms for High-Dimensional Integration and Approximation

We prove upper and lower error bounds for error of the randomized Smolyak algorithm and provide a thorough case study of applying the randomized Smolyak algorithm with the building blocks being quadratures based on scrambled nets for integration of functions coming from Haar-wavelets spaces. Moreover, we discuss different notions of negative dependence of randomized point sets which find applications in discrepancy theory and randomized quasi-Monte Carlo integration.

Rights

Use and reproduction:

No license. The provisions of the German Copyright Act (UrhG) apply.

Please note that individual components of the publication may be subject to other licensing or copyright conditions.

Cite

Citation style:
Could not load citation form.