Primal decomposition solves a problem by looking at individual components or subproblems and optimizing them independently. Dual decomposition solves a problem by looking at the dual problem, which is related to the original problem, and optimizing it instead. Primal decomposition generally requires fewer variables and is easier to understand, while dual decomposition is usually more efficient and has better convergence properties.