avoid sharp local minima and even speed up convergence.

In another attempt, @mm proposed the Majorization-Minimization (MM) framework. MM generalizes methods like EM by [“transferring”]{} the optimization to a sequence of surrogate functions (bounds) on the original objective function. The Concave-Convex Procedure (CCCP) [@cccp] is another widely-used instance of MM, where the surrogate function is obtained by *linearizing* the concave part of the objective function. Many successful machine learning algorithms employ CCCP, the Latent SVM [@dpm]. Other instances of MM algorithms include iterative scaling [@pietra97], and non-negative matrix factorization [@nmf]. Another related line of research concerns the Difference-of-Convex (DC) programming [@dc_prog], which can be shown to reduce to CCCP under certain conditions. Convergence properties of such general “bound optimization” algorithms have been discussed by @salakhutdinov02.

Despite widespread success, MM (and CCCP in particular) has a number of drawbacks, some of which have motivated our work. In practice, CCCP often exhibits *stickiness* to initialization, which necessitates expensive initialization or multiple trials [@naderi12; @Hyun14; @multifold]. In optimizing latent variable models, CCCP lacks the ability to incorporate application-specific information without making modifications to the objective function, such as prior knowledge or side information [@xing02; @yu12], latent variable uncertainty [@kumar12; @ping14], and posterior regularization 