In signal processing, aliasing is avoided by sending a signal through a low pass filter before sampling. Jack Poulson already explained one technique for non-uniform FFT using truncated Gaussians as ...

I use std::complex<> in my programs, and have to fight with compiler flags and workaround for each new compiler or compiler upgrade. I will try to recount these fights in chronological order: ...

The (famous) fast marching method for solving the Eikonal equation cannot be sped up by parallelization. There are other methods (for example fast sweeping methods) for solving the Eikonal equation ...

There is no need to convert to a OOP framework, in order to benefit from object orientation where it helps. Note that the number of functions is less of an indicator than code duplication within and ...

Your image of the numerical domain of dependence is correct. But try to also draw the analytical domain of dependence, maybe this could help you to better understand what is going on. Note that the ...

Nothing is wrong here. The Cuthill-McKee algorithm is a greedy algorithm, and doesn't depend too much on the order of A on input. The reverse Cuthill-McKee algorithm is often used to produce nice ...

For a sparse parallel solver, it's your own responsibility to provide a matrix vector product and a suitable preconditioner. The data for the vector itself should fit into main memory in any case. If ...

Here is an identical copy of an answer on MO: One intuitive way to understand a DAE is to interpret it as a dynamical system which can be controlled by some input signals, whose output signals have ...

The typical reason why a non-uniform can lead to higher accuracy is that the PDE to solve is not of the form $u_t(x,t)=u_{xx}(x,t)$, but of the form $u_t(x,t)=(D(x)u_x(x,t))_x$. If your non-uniform ...

The FFT can be used for periodic boundary conditions. Because von Neumann boundary conditions are effectively "mirror" boundary conditions, you have to do a "mirrored continuation", before you can ...

Monte Carlo simulations are the method of choice for computation of electron scattering. Tricks like importance sampling are used sometimes, so you might say it's not plain old Monte Carlo. But the ...

The Banach fixed-point theorem describes the standard situation when a fixed-point iteration is globally convergent. Especially the uniqueness part of the theorem indicates that you can only expect ...

My first idea for computing the eigenvalues of a Hermitian Toeplitz matrix would be to use the fast $O(n \log n)$ matrix multiplication for Toeplitz matrices together with the Lanczos algorithm to get ...

You certainly have a number of cursoriness mistakes in your derivation, but often only part of the mistake is propagated to later equations. $V=mgly_2$ should be $V=mgy_2$. $L=\frac{m}{2}\left(2\dot ... View answer 3 votes My advice would be to focus mainly on the memory consumption for the decision when to use single precision (float). So the bulk data for a FDTD computation should use float, but I would keep the ... View answer 3 votes The boundary of the domain has exactly two types of corners: convex 90 degree corners and concave 90 degree corners. For filling the domain with non-overlapping rectangles, only the concave corners ... View answer Accepted answer 2 votes How can this approach be recycled to also solve complex valued problems like every harmonic simulation? The difference of an harmonic simulation to a static simulation are bigger than just replacing ... View answer 2 votes There are two different ways to extent cubic splines to 2D. The more official approach is the tensor product approach. On the boundaries where$x$is constant, you require$S_{xx}=0=S_{xxy}$. On the ... View answer 2 votes What are good parametrizations of rational functions for response surface models? A widely used flexible parametrization of (piecewise) rational functions are non-uniform rational basis spline (NURBS)... View answer 2 votes My current implementation have two problems: I. I must keep the whole cost (slowness) function in RAM. This limits the size of the cost function I can use. II. I would like it to be even ... View answer 2 votes The control points of a Bézier curve are close to the curve, but not necessarily on the curve. This is exactly the same situation as for the approximation by Bernstein polynomials, and in fact the ... View answer 2 votes The computational cost depends not just on the number of DOF, but also on how efficient you can solve the resulting (nonlinear?) equation system. As an extreme case, the time harmonic Maxwell ... View answer 1 votes There is a very simple rule of thumb for the time step in FDTD: set it as large as possible while still satisfying the CFL condition. Basically your space discretization dicatates you your time step. ... View answer 1 votes You call this system an ODE (ordinary differential equation), but this sort of system is actually called an DAE (differential algebraic equation). What should I do to deal with above constraint? ... View answer Accepted answer 1 votes The time-dependent Schrödinger equation is not really a heat equation. Still, the Crank–Nicolson method is well suited for its solution. However, the Crank-Nicolson method is fully implicit, so the ... View answer 1 votes Building the Voronoï diagram of$n$points already takes time$O(n \log n)\$. I assume that what you want to compute is the intersection of the Voronoï diagram with the polygon. One way to compute this ...

Do you have box constraints, or do your constraints at least define a convex region? If not, they certainly can cause bad convergence. I looked up SA with downhill simplex in my copy of Num.recipes. ...

MINPACK uses a non-square QR factorization with pivoting, i.e. it's not a real QR decomposition (like the one in LAPACK), but a generalized more robust QRP decomposition (where P is the permutation ...