2013
DOI: 10.1137/120889642
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Multiscale Discrete Approximations of Fourier Integral Operators Associated with Canonical Transformations and Caustics

Abstract: Abstract. We develop an algorithm for the computation of general Fourier integral operators associated with canonical graphs. The algorithm is based on dyadic parabolic decomposition using wave packets and enables the discrete approximate evaluation of the action of such operators on data in the presence of caustics. The procedure consists in the construction of a universal operator representation through the introduction of locally singularity-resolving diffeomorphisms, enabling the application of wave packet… Show more

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Cited by 12 publications
(19 citation statements)
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“…We want to mention that numerical computations of FIOs by discretization is an important problem by itself, see, e.g., [4,1,5,3] which we do not study here. The emphasis of this paper however is different: how the sampling rate and/or the local averaging of the FIO affect the amount of microlocal data we collect and in turn how they could limit or not its microlocal inversion.…”
Section: Introductionmentioning
confidence: 99%
“…We want to mention that numerical computations of FIOs by discretization is an important problem by itself, see, e.g., [4,1,5,3] which we do not study here. The emphasis of this paper however is different: how the sampling rate and/or the local averaging of the FIO affect the amount of microlocal data we collect and in turn how they could limit or not its microlocal inversion.…”
Section: Introductionmentioning
confidence: 99%
“…Similarly, apply the pivoted QR factorization to K * Πrow,: and let Q row be the matrix of the first r columns of the Q matrix. 8 Let S col and S row be the index sets of a few extra randomly sampled columns and rows. Let J = Π col ∪ S col and I = Π row ∪ S row .…”
Section: Existing Low-rank Matrix Factorizationmentioning
confidence: 99%
“…6 for Each block partitioned by discontinuous point sets do 7 Set τ = 2π, since it is not necessary to detect discontinuity here. 8 Apply Algorithm 3 to recover the first row and the first column of each block. 9 Apply Algorithm 3 to recover the second and the third columns of each block.…”
Section: Algorithmmentioning
confidence: 99%
“…In the case of conjugate points, we use the semigroup property of S(t, s) and decompose the time step into smaller time steps such that in each step the formation of caustics is avoided. Numerically, the size of the smaller time steps can be determined by monitoring the rank-deficiency of W t 1 , see [11] for a more general point of view and Subsection 3.4 for an application.…”
Section: Oscillatory Integral Representationmentioning
confidence: 99%
“…2. This partitioning into time intervals is detected numerically from the points of rank-deficiency of the matrix W t 1 of the Hamiltonian system as detailed in [11] and indicated in Fig. 3 (right).…”
Section: Imaging Of Conormal Singularitiesmentioning
confidence: 99%