This paper is devoted to a thorough study on convex analysis approach to d.c. (difference of convex functions) programming and gives the State of the Art. Main results about d.c. duality, local and global optimalities in d.c. programming are presented. These materials constitute the basis of the DCA (d.c. algorithms). Its convergence properties have been tackled in detail, especially in d.c. polyhedral programming where it has finite convergence. Exact penalty, Lagrangian duality without gap, and regularization techniques have been studied to find appropriate d.c. decompositions and to improve consequently the DCA. Finally we present
the application of the DCA to solving a lot of important real-life d.c. programs.