In this talk, we review various subspace techniques that have been used in constructing numerical methods for solving nonlinear optimization problems. As large scale optimization problems are attracting more and more attention in recent years, subspace methods are getting more and more important due to the fact that subspace methods do not need to solve large scale subproblems in each iteration. The essential parts of a subspace method are how to construct subproblems defined in lower dimensional subspaces and how to choose the subspaces in which the subproblems are defined. Various subspace methods for unconstrained optimization, constrained optimization, nonlinear equations and nonlinear least squares, and matrix optimization problems are given respectively, and different proposals are made on how to choose the subspaces.