8,313 research outputs found
Energy preserving model order reduction of the nonlinear Schr\"odinger equation
An energy preserving reduced order model is developed for two dimensional
nonlinear Schr\"odinger equation (NLSE) with plane wave solutions and with an
external potential. The NLSE is discretized in space by the symmetric interior
penalty discontinuous Galerkin (SIPG) method. The resulting system of
Hamiltonian ordinary differential equations are integrated in time by the
energy preserving average vector field (AVF) method. The mass and energy
preserving reduced order model (ROM) is constructed by proper orthogonal
decomposition (POD) Galerkin projection. The nonlinearities are computed for
the ROM efficiently by discrete empirical interpolation method (DEIM) and
dynamic mode decomposition (DMD). Preservation of the semi-discrete energy and
mass are shown for the full order model (FOM) and for the ROM which ensures the
long term stability of the solutions. Numerical simulations illustrate the
preservation of the energy and mass in the reduced order model for the two
dimensional NLSE with and without the external potential. The POD-DMD makes a
remarkable improvement in computational speed-up over the POD-DEIM. Both
methods approximate accurately the FOM, whereas POD-DEIM is more accurate than
the POD-DMD
Order reduction approaches for the algebraic Riccati equation and the LQR problem
We explore order reduction techniques for solving the algebraic Riccati
equation (ARE), and investigating the numerical solution of the
linear-quadratic regulator problem (LQR). A classical approach is to build a
surrogate low dimensional model of the dynamical system, for instance by means
of balanced truncation, and then solve the corresponding ARE. Alternatively,
iterative methods can be used to directly solve the ARE and use its approximate
solution to estimate quantities associated with the LQR. We propose a class of
Petrov-Galerkin strategies that simultaneously reduce the dynamical system
while approximately solving the ARE by projection. This methodology
significantly generalizes a recently developed Galerkin method by using a pair
of projection spaces, as it is often done in model order reduction of dynamical
systems. Numerical experiments illustrate the advantages of the new class of
methods over classical approaches when dealing with large matrices
Deterministic Versus Randomized Kaczmarz Iterative Projection
Kaczmarz's alternating projection method has been widely used for solving a
consistent (mostly over-determined) linear system of equations Ax=b. Because of
its simple iterative nature with light computation, this method was
successfully applied in computerized tomography. Since tomography generates a
matrix A with highly coherent rows, randomized Kaczmarz algorithm is expected
to provide faster convergence as it picks a row for each iteration at random,
based on a certain probability distribution. It was recently shown that picking
a row at random, proportional with its norm, makes the iteration converge
exponentially in expectation with a decay constant that depends on the scaled
condition number of A and not the number of equations. Since Kaczmarz's method
is a subspace projection method, the convergence rate for simple Kaczmarz
algorithm was developed in terms of subspace angles. This paper provides
analyses of simple and randomized Kaczmarz algorithms and explain the link
between them. It also propose new versions of randomization that may speed up
convergence
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