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In this paper, we propose two novel channel matrix inversion methods, the approximate inverse (AI) and reduced inverse (RI) schemes, which reduce the recurrent complexity of the LS and JTLS schemes.
Several techniques were combined to simplify the original model, reduce its dimensions, decompose it into a number of small subproblems, and replace some parts of non-linear programming by matrix inversion methods.
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However, the proposed SIC based iterative matrix inversion method has the least number of operations when allocating energies.
Cross-validation computation time for ordinary kriging was reduced from 1 h to 3 min when we incorporated the matrix inversion method.
The bootstrap current density calculation module in the ACCOME code was used with the matrix inversion method without the large aspect ratio assumption.
The scheme uses the iterative energy and covariance matrix inversion method to produce system values and an upper bound for the system capacity.
It is proved that the use of such expressions leads to substantial savings in computer time when compared with the matrix inversion method.
Two matrices were solved for unknown time independent and time dependent constants by using matrix inversion method for an experimental climatic data of Varanasi, India and design parameters of BiSPVT system.
Here we offer a flexible, integrated system that employs a relational database to store point information, a modular system incorporating a choice of weather data interpolation methods, and a matrix inversion method that speeds computer calculations to display information on grids of any specified size, all with minimal user intervention.
The matrix inversion method provides a rapid spectral analysis for spectral imaging.
This matrix inversion method should be able to be implemented on a graphics programming unit (GPU) to attain another factor of 10 or more in speed.
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