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The main prerequisite is a strong motivation to undertake research related in modern aspects functional approximation theory, data compression, related algorithms, or the numerical analysis of partial differential equations.
A solid background in undergraduate analysis and partial differential equations, some basic functional or harmonic analysis, or numerical analysis will be useful.


Autumn 2020 (Monday, October 5 to Friday, December 11)


  • Live lecture hours: 10
  • Recorded lecture hours: 0
  • Total advised study hours: 40


  • Mon 14:05 - 14:55


Requirements are standard year 3 or master's level Analysis and some knowledge of elliptic partial differential equations.
Exposure to Galerkin or finite element methods (as taught in spring term MAGIC-100 or equivalent) will be helpful though not essential. "Review" material will be uploaded.


We start by reviewing the standard Galerkin method with a focus on numerical approximation methods such as wavelet Galekrin, finite elements and discontinuous Galerkin for elliptic and parabolic equations, including the needed element of functional analysis, e.g., Sobolev and Besov spaces. We then recall the apriori error analysis of such methods and move onto aposteriori error analysis. We follow up this with an overview of the literature on adaptive methods and their convergence analysis with a focus on complexity of algorithms. Time allowing we look at connections between wavelet and Galerkin methods or space-time methods for parabolic (perhaps hyperbolic) problems. (NB to be reduced to 10 hours)


Omar Lakkis (main contact)
Phone 01273 876812
Photo of Omar Lakkis
Chandrasekhar Venkataraman
Phone 01273 876617


MAGIC-098-AFEM 2019 boardshotsOmar Lakkis and Chandrasekhar Venkataraman
Finite elements: theory, fast solvers, and applications in elasticity theoryDietrich Braess
A posteriori error estimation techniques for finite element methodsRüdiger Verfürth
iFEM and adaptive finite element computational package in Matlab (R)Long Chen
Convergence of adaptive finite element methodsMorin, P., R. Nochetto and K. Siebert
Adaptive finite element methods with convergence ratesBinev, Peter, Wolfgang Dahmen and Ron DeVore
Approximation and learning by greedy algorithmsBarron, Andrew R. et al.
Quasi-optimal convergence rate for an adaptive fi- nite element methodCascon, J. Manuel et al.
Axioms of AdaptivityCarstensen, Carsten et al.


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Recorded Lectures

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