This paper is concerned with the construction of an adaptive beamformer, which uses the dominant subspace information of a sample covariance matrix. We assume that the sample covariance matrix and its eigenvalue decomposition (EVD) are to be updated as data arrive. We present a unified structure for updating the EVD and introduce a robust method for adapting the rank of the dominant subspace. We use the EVD of the sample covariance matrix to build a fast rank-and weight-adaptive beamformer. A useful component of the beamforming routine is a fast algorithm for updating the estimates of the direction of arrival of the dominant sources.