Extension and optimization of the FIND algorithm: Computing Greens and less-than Greens functions
The FIND algorithm is a fast algorithm designed to calculate certain entries of the inverse of a sparse matrix. Such calculation is critical in many applications, e.g., quantum transport in nano-devices. We extended the algorithm to other matrix inverse related calculations. Those are required for example to calculate the less-than Greens function and the current density through the device. For a 2D device discretized as an NxNy mesh, the best known algorithms have a running time of O ( N x 3 N y ) , whereas FIND only requires O ( N x 2 N y ) . Even though this complexity has been reduced by an order of magnitude, the matrix inverse calculation is still the most time consuming part in the simulation of transport problems. We could not reduce the order of complexity, but we were able to significantly reduce the constant factor involved in the computation cost. By exploiting the sparsity and symmetry, the size of the problem beyond which FIND is faster than other methods typically decreases from a 130130 2D mesh down to a 4040 mesh. These improvements make the optimized FIND algorithm even more competitive for real-life applications.
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