variable models and in clustering problems, MM algorithms such as CCCP and [$k$-means]{}are known to be sensitive to the initial values of the latent variables or cluster memberships. We refer to this problem as *stickiness* of the algorithm to the initial latent values. Our experimental results show that [G-MM]{}leads to methods that tend to be less sticky to initialization. We demonstrate the benefit of using [G-MM]{}on multiple problems, including [$k$-means]{}clustering and applications of [Latent Structural SVM]{}[s]{} to image classification with latent variables.

Related Work
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Perhaps the most famous iterative algorithm for non-convex optimization in machine learning and statistics is the EM algorithm [@em]. EM is best understood in the context of maximum likelihood estimation in the presence of missing data, or *latent variables*. EM is a bound optimization algorithm: in each E-step, a lower bound on the likelihood is constructed, and the M-step maximizes this bound.

Countless efforts have been made to extend the EM algorithm since its introduction. In [@gen_em] it is shown that, while both steps in EM involve optimizing some functions, it is not necessary to fully optimize the functions in each step; in fact, each step only needs to [“make progress”]{}. This relaxation can potentially 