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Quantitative Robust Uncertainty Principles and Optimally Sparse Decompositions
Uncertainty principle applications of uncertainty principles random matrices eigenvalues of random matrices sparsity trigonometric expansion convex optimization duality in optimization basis pursuit wavelets linear programming
2015/6/17
In this paper, we develop a robust uncertainty principle for finite signals in CN which states that for nearly all choices T,Ω ⊂ {0, . . . , N − 1} such that |T| + |Ω| (log N...
Quantitative Stratification and the Regularity of Harmonic Maps and Minimal Currents
Quantitative Stratification the Regularity of Harmonic Maps Minimal Currents
2011/9/9
Abstract: We introduce techniques for turning estimates on the infinitesimal behavior of solutions to nonlinear equations (statements concerning tangent cones and blow ups) into more effective control...
A quantitative isoperimetric inequality for fractional perimeters
quantitative isoperimetric inequality fractional perimeters
2011/1/14
Recently Frank & Seiringer have shown an isoperimetric inequality for nonlocal perimeter functionals arising from Sobolev seminorms of fractional order. This isoperimetric inequality is improved here ...