This paper addresses the problem of source and relay transmit covariance optimization on the Gaussian MIMO relay channel with full channel state information (CSI), i.e., assuming perfect knowledge of all channels. For full-duplex relaying, we show that the cut-set bound on capacity can be computed as the solution of a convex problem, thus providing a tighter bound than previously published. For time division duplex (TDD) relaying, both upper and lower bounds on capacity are derived, and the transmit covariance matrices are optimized for decode-and-forward (DF) strategies with either partial or full decoding at the relay. A generic procedure is introduced to formulate these problems into a standard convex form, and to solve them efficiently. Suboptimum precoders are also proposed which have a specific matrix structure that either leads to a closed-form expression or at least reduces the dimension of the optimization problem. Practical aspects related to transmit power constraints and CSI availability are then discussed. Finally, simulations in a cellular downlink scenario show that the partial DF strategy can achieve a rate very close to capacity for realistic values of the source to relay SNR, and that the rate loss due to suboptimum precoder structures remains small for typical antenna configurations.