Thomas Strohmer spends much of his time researching Algorithm, Compressed sensing, Convex optimization, Artificial intelligence and Phase retrieval. He has included themes like Transmitter, MIMO, Beamforming and Control theory in his Algorithm study. While the research belongs to areas of Control theory, Thomas Strohmer spends his time largely on the problem of Rayleigh fading, intersecting his research to questions surrounding Codebook and Grassmannian.
His Compressed sensing research is multidisciplinary, incorporating perspectives in Sparse matrix, Modulo, Signal processing, Perspective and Antenna. His Artificial intelligence research incorporates elements of Radar, Upper and lower bounds, Computer vision and Pattern recognition. His Phase retrieval research includes themes of Noise, Unit sphere and Matrix completion.
His main research concerns Algorithm, Compressed sensing, Discrete mathematics, Pure mathematics and Mathematical optimization. In his papers, Thomas Strohmer integrates diverse fields, such as Algorithm and Convex optimization. As a part of the same scientific study, he usually deals with the Compressed sensing, concentrating on Radar and frequently concerns with Upper and lower bounds and Computer vision.
As a member of one scientific family, he mostly works in the field of Discrete mathematics, focusing on Matrix and, on occasion, Linear least squares, Linear subspace, Inverse problem and Least squares. The study incorporates disciplines such as Zak transform, Gabor transform and Algebra in addition to Pure mathematics. His Mathematical optimization study integrates concerns from other disciplines, such as Signal reconstruction, Blind deconvolution, Sparse matrix, Sparse approximation and Applied mathematics.
Blind deconvolution, Algorithm, Discrete mathematics, Convex optimization and Calibration are his primary areas of study. His Blind deconvolution study combines topics from a wide range of disciplines, such as Gradient descent, Mathematical optimization, Robustness and Image processing. His work carried out in the field of Algorithm brings together such families of science as Algebraic connectivity, Laplacian matrix, Kernel, Spectral clustering and Cut.
His Discrete mathematics research incorporates themes from Connection, Inverse problem, Matrix, Linear least squares and Efficient algorithm. His Convex optimization research includes elements of Compressed sensing, Mathematical analysis, Astronomical imaging, Matrix completion and Phase problem. His biological study spans a wide range of topics, including Key and Computer engineering.
His primary areas of study are Blind deconvolution, Algorithm, Discrete mathematics, Convex optimization and Noise. Thomas Strohmer interconnects Phase problem, Fourier transform and Matrix completion in the investigation of issues within Algorithm. His work deals with themes such as Image processing and Total least squares, Least squares, Non-linear least squares, which intersect with Discrete mathematics.
His Convex optimization research incorporates elements of Phase retrieval, Mathematical analysis, Degrees of freedom, Limit and Sequence. His studies deal with areas such as Subspace topology, Maxima and minima, Gradient descent, Convolution and Optimization problem as well as Noise. He combines subjects such as Wireless and Mathematical optimization with his study of Maxima and minima.
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Grassmannian beamforming for multiple-input multiple-output wireless systems
D.J. Love;R.W. Heath;T. Strohmer.
international conference on communications (2003)
High-Resolution Radar via Compressed Sensing
M.A. Herman;T. Strohmer.
IEEE Transactions on Signal Processing (2009)
PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming
Emmanuel J. Candès;Thomas Strohmer;Vladislav Voroninski.
Communications on Pure and Applied Mathematics (2013)
Gabor Analysis and Algorithms: Theory and Applications
Hans G. Feichtinger;T. Strohmer.
GRASSMANNIAN FRAMES WITH APPLICATIONS TO CODING AND COMMUNICATION
Thomas Strohmer;Robert W Heath.
Applied and Computational Harmonic Analysis (2003)
Phase Retrieval via Matrix Completion
Emmanuel J. Candès;Yonina C. Eldar;Thomas Strohmer;Vladislav Voroninski.
Siam Review (2015)
A Randomized Kaczmarz Algorithm with Exponential Convergence
Thomas Strohmer;Roman Vershynin.
Journal of Fourier Analysis and Applications (2009)
Designing structured tight frames via an alternating projection method
J.A. Tropp;I.S. Dhillon;R.W. Heath;T. Strohmer.
IEEE Transactions on Information Theory (2005)
General Deviants: An Analysis of Perturbations in Compressed Sensing
M.A. Herman;T. Strohmer.
IEEE Journal of Selected Topics in Signal Processing (2010)
Efficient numerical methods in non-uniform sampling theory
Hans G. Feichtinger;Karlheinz Gröchenig;Thomas Strohmer.
Numerische Mathematik (1995)
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