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We apply a source-receiver compression approach to reduce the computational time and memory usage of the nonlinear inversion approaches for interpreting three-dimensional microwave data. By detecting and quantifying the extent of redundancy in the data, we assemble a reduced set of simultaneous sources and receivers that are weighted sums of the physical sources and receivers employed in the measurement setup. Because the number of these simultaneous sources and receivers can be significantly less than those of the physical sources and receivers, the computational time and memory usage of any inversion method such as steepest-descent, nonlinear conjugate-gradient, contrast-source inversion, and quasi-Newton can be tremendously reduced. The scheme is based on decomposing the data into their principal components using a singular-value decomposition approach and the data compression is done through the elimination of eigenvectors corresponding to small eigenvalues. Consequently, this will also suppress the effect of noise in the data. As a concept demonstration we show that this approach has the potential of significantly reducing both computational time and memory usage of the Gauss-Newton inversion method by few orders of magnitude.