This report analyzes and compares Barycentric Lagrange interpolation to Cardinal Trigonometric interpolation, with regards to computational cost and accuracy. It also covers some edge case scenarios which may interfere with the accuracy and stability. Later on, these two interpolation methods are applied on parameterized curves and surfaces, to compare and contrast differences with the standard one dimensional scenarios. The report also contains analysis of and comparison with regular Lagrange interpolation. The report concludes that Barycentric Lagrange interpolation is generally speaking more computationally efficient, and that the inherent need for periodicity makes Cardinal Trigonometric interpolation less reliable in comparison. On the other hand, Barycentric Lagrange interpolation is difficult to implement for higher dimensional problems, and also relies heavily on Chebyshev spaced nodes, something which can cause issues in a practical application of interpolation. Given ideal scenarios, Cardinal Trigonometric interpolation is more accurate, and for periodic functions generally speaking better than Barycentric Lagrange interpolation. Regular Lagrange interpolation is found to be unviable due to the comparatively big computational cost.

This thesis aims to provide a brief exposition of some chosen modes of convergence; namely uniform convergence, pointwise convergence and L1 convergence. Theoretical discussion is complemented by simple applications to scientific computing. The latter include solving differential equations with various methods and estimating the convergence, as well as modelling problematic situations to investigate odd behaviors of usually convergent methods.