multiparameter eigenvalue problems arising in applications, such as the model updating problems, bivariate polynomial systems, aeroelastic flutter problems are often sparse and singular. the theoretical analysis and numerical algorithms for this problem are fundamental different from those for the nonsingular problems. in this talk, i will give two classes of methods to solve a singular multiparameter eigenvalue problem, one is the numerical method by transforming it to a simultaneous eigenvalue problem, the other is the homotopy method. for the former, we apply the row-column compress method to reduce the size of the problem. for the latter, to overcome the difficulty of computational complexity, we use random product homotopy to cut down the number of invalid paths. moreover, a more accurate upper bound of the number of solutions, i.e. the number of generated paths, is presented. also one practical example and several random examples are presented to validate the effectiveness and efficiency of the method.