Topic
A priori estimate
About: A priori estimate is a research topic. Over the lifetime, 1090 publications have been published within this topic receiving 20892 citations.
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01 Jan 1977
TL;DR: In this article, a priori estimation of the gradient of the Bernstein problem is given. But the gradient is not a priorimate of the radius of the singular set, and it is not known whether the gradient can be estimated by direct methods.
Abstract: I: Parametric Minimal Surfaces.- 1. Functions of Bounded Variation and Caccioppoli Sets.- 2. Traces of BV Functions.- 3. The Reduced Boundary.- 4. Regularity of the Reduced Boundary.- 5. Some Inequalities.- 6. Approximation of Minimal Sets (I).- 7. Approximation of Minimal Sets (II).- 8. Regularity of Minimal Surfaces.- 9. Minimal Cones.- 10. The First and Second Variation of the Area.- 11. The Dimension of the Singular Set.- II: Non-Parametric Minimal Surfaces.- 12. Classical Solutions of the Minimal Surface Equation.- 13. The a priori Estimate of the Gradient.- 14. Direct Methods.- 15. Boundary Regularity.- 16. A Further Extension of the Notion of Non-Parametric Minimal Surface.- 17. The Bernstein Problem.- Appendix A.- Appendix B.- Appendix C.
2,479 citations
TL;DR: In this paper, the critical blow-up exponent for a Keller-Segel-type chemotaxis model was determined, where the chemotactic sensitivity equals some nonlinear function of the particle density.
801 citations
TL;DR: An interscale orthonormal wavelet thresholding algorithm is described based on this new approach and its near-optimal performance is described by comparing it with the results of three state-of-the-art nonredundant denoising algorithms on a large set of test images.
Abstract: This paper introduces a new approach to orthonormal wavelet image denoising. Instead of postulating a statistical model for the wavelet coefficients, we directly parametrize the denoising process as a sum of elementary nonlinear processes with unknown weights. We then minimize an estimate of the mean square error between the clean image and the denoised one. The key point is that we have at our disposal a very accurate, statistically unbiased, MSE estimate-Stein's unbiased risk estimate-that depends on the noisy image alone, not on the clean one. Like the MSE, this estimate is quadratic in the unknown weights, and its minimization amounts to solving a linear system of equations. The existence of this a priori estimate makes it unnecessary to devise a specific statistical model for the wavelet coefficients. Instead, and contrary to the custom in the literature, these coefficients are not considered random any more. We describe an interscale orthonormal wavelet thresholding algorithm based on this new approach and show its near-optimal performance-both regarding quality and CPU requirement-by comparing it with the results of three state-of-the-art nonredundant denoising algorithms on a large set of test images. An interesting fallout of this study is the development of a new, group-delay-based, parent-child prediction in a wavelet dyadic tree
641 citations
TL;DR: In this paper, the authors describe bounded energy sequences of solutions to the linear wave equation (1) in terms of their energy, up to remainder terms small in energy norm and in every Strichartz norm.
Abstract: This work is devoted to the description of bounded energy sequences of solutions to the equation (1) □ u + | u |4 = 0 in [inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="01i" /], up to remainder terms small in energy norm and in every Strichartz norm. The proof relies on scattering theory for (1) and on a structure theorem for bounded energy sequences of solutions to the linear wave equation. In particular, we infer the existence of an a priori estimate of Strichartz norms of solutions to (1) in terms of their energy.
568 citations
TL;DR: This work studies approximations of optimization problems with probabilistic constraints in which the original distribution of the underlying random vector is replaced with an empirical distribution obtained from a random sample to obtain a lower bound to the true optimal value.
Abstract: We study approximations of optimization problems with probabilistic constraints in which the original distribution of the underlying random vector is replaced with an empirical distribution obtained from a random sample. We show that such a sample approximation problem with a risk level larger than the required risk level will yield a lower bound to the true optimal value with probability approaching one exponentially fast. This leads to an a priori estimate of the sample size required to have high confidence that the sample approximation will yield a lower bound. We then provide conditions under which solving a sample approximation problem with a risk level smaller than the required risk level will yield feasible solutions to the original problem with high probability. Once again, we obtain a priori estimates on the sample size required to obtain high confidence that the sample approximation problem will yield a feasible solution to the original problem. Finally, we present numerical illustrations of how these results can be used to obtain feasible solutions and optimality bounds for optimization problems with probabilistic constraints.
568 citations