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Embedding Differential Dynamic Logic in PVS

Autor: Slagel, J. Tanner, Moscato, Mariano, White, Lauren, Muñoz, César A., Balachandran, Swee, Dutle, Aaron

Publikováno v: EPTCS 402, 2024, pp. 43-62

Differential dynamic logic (dL) is a formal framework for specifying and reasoning about hybrid systems, i.e., dynamical systems that exhibit both continuous and discrete behaviors. These kinds of systems arise in many safety- and mission-critical ap

Externí odkaz: http://arxiv.org/abs/2404.15214

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Sampled Limited Memory Methods for Massive Linear Inverse Problems

Autor: Chung, Julianne, Chung, Matthias, Slagel, J. Tanner, Tenorio, Luis

In many modern imaging applications the desire to reconstruct high resolution images, coupled with the abundance of data from acquisition using ultra-fast detectors, have led to new challenges in image reconstruction. A main challenge is that the res

Externí odkaz: http://arxiv.org/abs/1912.07962

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Sampled Tikhonov Regularization for Large Linear Inverse Problems

Autor: Slagel, J. Tanner, Chung, Julianne, Chung, Matthias, Kozak, David, Tenorio, Luis

In this paper, we investigate iterative methods that are based on sampling of the data for computing Tikhonov-regularized solutions. We focus on very large inverse problems where access to the entire data set is not possible all at once (e.g., for pr

Externí odkaz: http://arxiv.org/abs/1812.06165

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Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems

Autor: Chung, Julianne, Chung, Matthias, Slagel, J. Tanner, Tenorio, Luis

We describe stochastic Newton and stochastic quasi-Newton approaches to efficiently solve large linear least-squares problems where the very large data sets present a significant computational burden (e.g., the size may exceed computer memory or data

Externí odkaz: http://arxiv.org/abs/1702.07367

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