Many disciplines, such as antenna beam-forming and \nadaptive optics, require the ability to perform high \nspeed matrix operations, which often can be reduced to a \nseries of matrix-vector products. Analog optical matrixvector \nprocessors (OMVP's) take advantage of the high \nspeed and parallelism of optics to rapidly compute \nmatrix-vector products. However, OMVP's suffer from a \nlow accuracy which currently hinders their widespread \napplication. Over the past decade, various techniques \nhave been introduced to achieve high accuracy computation \nwith OMVP's. This dissertation studies the application \nof error-correcting codes (ECC's) and the union of ECC's \nwith digital partitioning to achieve high accuracy computation. \nThe ability of ECC's to improve the performance \nof OMVP's is studied through the use of computer simulations. \nThe effect ECC's have on system throughput is \nalso studied. Digital partitioning is a new technique \nfor achieving high accuracy computations on analog \nOMVP's. One potential drawback of digital partitioning \nis its sensitivity to errors. Error-correcting codes can \nenhance the performance of the digital partitioning algorithm \nby reducing this sensitivity to errors. Computer \nsimulation results are presented showing the benefits of \nusing ECC's with digital partitioning. In addition, an \nanalysis of the time requirements for encoding and decoding \nthe ECC's and the effect on system throughput is \npresented.