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, P. Samoraj, Automatic approximation using asyptotically optimal adaptive interpolation, Numerical Algorithms (2021), DOI 10.1007/s11075-021-01114-9
  2. P. Kritzer, F. Pillichshammer, L. Plaskota, G.W. Wasilkowski, On efficient weighted integration via a change of variables, Numerische Mathematik 146 (2020), pp.545-570, DOI 10.1007/s00211-020-01147-7
  3. P. Morkisz, L. Plaskota, Complexity of approximating Holder classes from information with varying Gaussian noise, Journal of Complexity 60 (2020) 101497, DOI 10.1016/j.jco.2020.101497
  4. P. Kritzer, F. Pillichshammer, L. Plaskota. G.W. Wasilkowski, On alternative quantization for doubly weighted approximation and integration over unbounded domains, Journal of Approximation Theory, 256 (2020) 105433, DOI 10.1016/j.jat.2020.105433
  5. L. Plaskota. P. Siedlecki, H. Wozniakowski, Absolute value information for IBC problems, Journal of Complexity 56 (2020) 101427.
  6. L. Plaskota, Linear versus nonlinear approximation in the average case setting, in Contemporary Computational Mathematics - a celebration of the 80th birthday of Ian Sloan (J. Dick, F.Y. Kuo, H. Wozniakowski, eds.), Springer-Verlag, 2018, pp.1035-1049.
  7. 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/
  8. 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
  9. 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
  10. L. Plaskota, Automatic integration using asymptotically optimal adaptive Simpson quadrature, Numerische Mathematik, 131 (2015), pp.173-198, DOI 10.1007/s00211-014-0684-3
  11. 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
  12. 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.
  13. 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.
  14. 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.
  15. L. Plaskota, G.W. Wasilkowski, Tractability of infinite-dimensional integration in the worst case and randomized settings, J. Complexity 27 (2011), pp.505-518.
  16. L. Plaskota, G.W. Wasilkowski, The power of adaption for functions with singularities, J. Fixed Point Theory and Appl., 6 (2009), pp.227-248.
  17. L. Plaskota, G.W. Wasilkowski, Y. Zhao, New averaging technique for approximating weighted integrals, J. Complexity 25 (2009), pp.268-291.
  18. L. Plaskota, G.W. Wasilkowski, Uniform approximation of piecewise r-smooth and globally continuous functions, SIAM J. Numer. Analysis 47 (2009), pp.762-785
  19. L. Plaskota, G.W. Wasilkowski, Y. Zhao, The power of adaption for approximating functions with singularities, Mathematics of Computation 77 (2008), pp.2309-2338.
  20. L. Plaskota, G.W. Wasilkowski, Adaption allows efficient integration of functions with unknown singularities, Numerische Mathematik 102 (2005), pp. 123-144.
  21. M.A. Kon, L. Plaskota, Information-based nonlinear approximation: an average case setting, J. Complexity 21 (2005), pp.211-229.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. L. Plaskota, K. Ritter, G.W. Wasilkowski, Average case complexity of weighted approximation and integration over R+, J. Complexity 18 (2001), pp.517-544.
  27. L. Plaskota G.W. Wasilkowski, The exact exponent of sparse grid quadratures in the weighted case, J. Complexity 17 (2001), pp.840-849.
  28. M.A. Kon L. Plaskota, Complexity of neural network approximation with limited information: a worst case approach, J. Complexity 17 (2001), pp.345-365.
  29. 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.
  30. M.A. Kon L. Plaskota, Information complexity of neural networks, Neural Networks 13 (2000), pp.365-376.
  31. L. Plaskota, The exponent of discrepancy of sparse grids is at least 2.1933, Advances in Comput. Math. 12 (2000), pp.2-24.
  32. L. Plaskota, Average case uniform approximation in the presence of Gaussian noise, J. Approx. Theory 93 (1998), pp. 501-515.
  33. L. Plaskota, Worst case complexity of problems with random information noise, J. Complexity 12 (1996), pp. 416-439.
  34. 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.
  35. L. Plaskota, How to benefit from noise, J. Complexity 12 (1996), pp. 175-184.
  36. 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.
  37. L. Plaskota, Average complexity for linear problems in a model with varying information noise, J. Complexity 11 (1995), pp. 240-264.
  38. L. Plaskota, Average case approximation of linear functionals based on information with deterministic noise, J. Computing and Information 4 (1994), pp. 21-39.
  39. L. Plaskota, A note on varying cardinality in the average case setting, J. Complexity 9 (1993), pp.458-470.
  40. L. Plaskota, Optimal approximation of linear operators based on noisy data on functionals, J. Approx. Theory 73 (1993), pp.93-105.
  41. B.Z. Kacewicz, L. Plaskota, The minimal cost of approximating linear operators using perturbed information, J. Complexity 9 (1993), pp.113-134.
  42. L. Plaskota, Function approximation and integration on the Wiener space with noisy data, J. Complexity 8 (1992), pp.301-323.
  43. B.Z. Kacewicz, L. Plaskota, Termination conditions for approximating linear problems with noisy information, Math. of Comput. 59 (1992), pp.503-513.
  44. B.Z. Kacewicz, L. Plaskota, Noisy information for linear problems in the asymptotic setting, J. Complexity 7 (1991), pp.35-57.
  45. 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.
  46. L. Plaskota, On average case complexity of linear problems with noisy information, J. Complexity 6 (1990), pp.199-230.
  47. L. Plaskota, Asymptotic error for the global maxima of functions in $s$ dimensions, J. Complexity 5 (1989), pp.369-378.
  48. 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.