Leszek Plaskota

Research Monograph:

  1. L. Plaskota, "Noisy Information and Computational Complexity", Cambridge University Press, Cambridge, 1996.

Edited Volumes:

  1. "Monte Carlo and quasi-Monte Carlo Methods 2010". Procs. of the 9th Intern. Conf. on Monte Carlo and quasi-Monte Carlo Methods in Scientific Computing, Warsaw 2010. Eds.: L. Plaskota and H. Wozniakowski. Springer 2012.
  2. Procs. of "Algorithms and complexity for continuous problems", Dagstuhl 2009. Eds.: T. Mueller-Gronbach, L. Plaskota, W.Ch. Schmid. J. Complexity, 27(3-4), 2011 (special issue).
  3. "Festschrift for the 60th Birthday of Henryk Wozniakowski". Eds.: B.Z. Kacewicz, L. Plaskota, G.W. Wasilkowski. J. Complexity, 23(4-6), 2006 (special issue).
  4. "Festschrift for the 70th Birthday of Joseph F. Traub". Eds.: L. Plaskota, K. Ritter. J. Complexity, 20(5), 2004 (special issue).


  1. L. Plaskota, Linear versus nonlinear approximation in the average case setting, accepted.
  2. F.Y. Kuo, D. Nuyens, L. Plaskota, I.H. Sloan, G.W. Wasilkowski, Infinite-dimensional integration and the multivariate decomposition method, Journal of Computational and Applied Mathematics 326 (2017), pp.217-234, DOI:10.1016/
  3. P. Morkisz, L. Plaskota, Approximation of piecewise Holder functions from inexact information, J. Complexity, 32 (2016), pp.122-136, DOI:10.1016/j.jco.2015.09.002.
  4. F.Y. Kuo, L. Plaskota, G.W. Wasilkowski, Optimal algorithms for doubly weighted approximation of univariate functions, J. Approx. Theory, 201 (2016), pp.30-47, DOI:10.1016/j.jat.2015.08.007.
  5. L. Plaskota, Automatic integration using asymptotically optimal adaptive Simpson quadrature, Numerische Mathematik, 131 (2015), pp.173-198, DOI: 10.1007/s00211-014-0684-3.
  6. L. Plaskota, G.W. Wasilkowski, Efficient algorithms for multivariate and infinite-variate integration with exponential weight, Numerical Algorithms 67 (2014), pp.385-403, DOI: 10.1007/s11075-013-9798-4.
  7. L. Plaskota, Continuous problems: optimality, complexity, tractability, Computer Algebra in Scientific Computing, V.P. Gerdt, W. Koepf, W.M. Seiler, E.V. Vorozhtsov (Eds.), LNCS 8660, Springer 2014, pp.357-372.
  8. L. Plaskota, Noisy information: optimality, complexity, tractability, Monte Carlo and quasi-Monte Carlo Methods 2012, J. Dick, F.Y. Kuo, G.W. Peters, I.H. Sloan (Eds.), Springer 2013, pp.173-209.
  9. L. Plaskota, G.W. Wasilkowski, Y. Zhao, An adaptive algorithm for weighted approximation of singular functions over R, SIAM J. Numer. Analysis 51 (2013), pp.1470-1493.
  10. L. Plaskota, G.W. Wasilkowski, Tractability of infinite-dimensional integration in the worst case and randomized settings, J. Complexity 27 (2011), pp.505-518.
  11. L. Plaskota, G.W. Wasilkowski, The power of adaption for functions with singularities, J. Fixed Point Theory and Appl., 6 (2009), pp.227-248.
  12. L. Plaskota, G.W. Wasilkowski, Y. Zhao, New averaging technique for approximating weighted integrals, J. Complexity 25 (2009), pp.268-291.
  13. L. Plaskota, G.W. Wasilkowski, Uniform approximation of piecewise r-smooth and globally continuous functions, SIAM J. Numer. Analysis 47 (2009), pp.762-785
  14. L. Plaskota, G.W. Wasilkowski, Y. Zhao, The power of adaption for approximating functions with singularities, Mathematics of Computation 77 (2008), pp.2309-2338.
  15. L. Plaskota, G.W. Wasilkowski, Adaption allows efficient integration of functions with unknown singularities, Numerische Mathematik 102 (2005), pp. 123-144.
  16. M.A. Kon, L. Plaskota, Information-based nonlinear approximation: an average case setting, J. Complexity 21 (2005), pp.211-229.
  17. L. Plaskota, G.W. Wasilkowski, Smolyak's algorithm for integration and L1-approximation of multivariate functions with bounded mixed derivatives of second order, Numerical Algorithms 36 (2004), pp.229-246.
  18. P. Gajda, Y. Li, L. Plaskota, G.W. Wasilkowski, A Monte Carlo algorithm for average case weighted integration over Rd, Math. Comp. 73 (2004), pp.813-825.
  19. L. Plaskota, K. Ritter, G.W. Wasilkowski, Optimal designs for weighted approximation and integration of stochastic processes on R+, J. Complexity 20 (2004), pp.108-131.
  20. L. Plaskota, K. Ritter, G.W. Wasilkowski, Average case complexity of weighted integration and approximation over Rd with isotropic weight, in Proc. of MCQMC 2002, Hong-Kong 2000, Springer 2002, pp.446-459.
  21. L. Plaskota, K. Ritter, G.W. Wasilkowski, Average case complexity of weighted approximation and integration over R+, J. Complexity 18 (2001), pp.517-544.
  22. L. Plaskota G.W. Wasilkowski, The exact exponent of sparse grid quadratures in the weighted case, J. Complexity 17 (2001), pp.840-849.
  23. M.A. Kon L. Plaskota, Complexity of neural network approximation with limited information: a worst case approach, J. Complexity 17 (2001), pp.345-365.
  24. L. Plaskota, G.W. Wasilkowski, H. Wozniakowski, A new algorithm and worst case complexity for Feynman-Kac path integration, J. Comput. Physics 164 (2000), pp.335-353.
  25. M.A. Kon L. Plaskota, Information complexity of neural networks, Neural Networks 13 (2000), pp.365-376.
  26. L. Plaskota, The exponent of discrepancy of sparse grids is at least 2.1933, Advances in Comput. Math. 12 (2000), pp.2-24.
  27. L. Plaskota, Average case uniform approximation in the presence of Gaussian noise, J. Approx. Theory 93 (1998), pp. 501-515.
  28. L. Plaskota, Worst case complexity of problems with random information noise, J. Complexity 12 (1996), pp. 416-439.
  29. L. Plaskota, Survey of computational complexity with noisy information, in ``The Mathematics of Numerical Analysis'', vol. 32 (1996), Proc. of 1995 AMS-SIAM Summer Seminar in Appl. Math., Park City, Utah, ser. Lecture in Appl. Math., eds. J. Renegar, M. Shub, S. Smale, pp. 651-664.
  30. L. Plaskota, How to benefit from noise, J. Complexity 12 (1996), pp. 175-184.
  31. L. Plaskota, Complexity of problems with noisy information, in ``Applied Stochastic and Optimization'', Special Issues of Zeitschrift für Angewandte Mathematik und Mechanik (ZAMM), Issue 3, O. Mohrenholtz, K. Morti, R. Mennicken (eds.), Proc. of ICIAM/JuneGAMM 95 Symposium in Hamburg, Germany, pp. 116-120.
  32. L. Plaskota, Average complexity for linear problems in a model with varying information noise, J. Complexity 11 (1995), pp. 240-264.
  33. L. Plaskota, Average case approximation of linear functionals based on information with deterministic noise, J. Computing and Information 4 (1994), pp. 21-39.
  34. L. Plaskota, A note on varying cardinality in the average case setting, J. Complexity 9 (1993), pp.458-470.
  35. L. Plaskota, Optimal approximation of linear operators based on noisy data on functionals, J. Approx. Theory 73 (1993), pp.93-105.
  36. B.Z. Kacewicz, L. Plaskota, The minimal cost of approximating linear operators using perturbed information, J. Complexity 9 (1993), pp.113-134.
  37. L. Plaskota, Function approximation and integration on the Wiener space with noisy data, J. Complexity 8 (1992), pp.301-323.
  38. B.Z. Kacewicz, L. Plaskota, Termination conditions for approximating linear problems with noisy information, Math. of Comput. 59 (1992), pp.503-513.
  39. B.Z. Kacewicz, L. Plaskota, Noisy information for linear problems in the asymptotic setting, J. Complexity 7 (1991), pp.35-57.
  40. B.Z. Kacewicz, L. Plaskota, On the minimal cost of approximating linear problems based on information with deterministic noise, Numer. Funct. Anal. and Optimiz. 11 (1990), pp.511-528.
  41. L. Plaskota, On average case complexity of linear problems with noisy information, J. Complexity 6 (1990), pp.199-230.
  42. L. Plaskota, Asymptotic error for the global maxima of functions in $s$ dimensions, J. Complexity 5 (1989), pp.369-378.
  43. L. Plaskota, Optimal linear information for the search for the maximum of real functions (in Russian), Zh. Vychisl. Mat. i Mat. Fiz. 26 (1986), pp.934-938.