Introduction
The proximal point method is a conceptually simple algorithm for minimizing a function \(f\) on \({\mathbb R}^d\). Given an iterate \(x_t\), the method defines \(x_{t+1}\) to be any minimizer of the proximal subproblem
\[\underset{x}{\operatorname{argmin}}~\left\{f(x)+\tfrac{1}{2\nu}\xx_t\^2\right\},\]for an appropriately chosen parameter \(\nu>0\). At first glance, each proximal subproblem seems no easier than minimizing \(f\) in the first place. On the contrary, the addition of the quadratic penalty term often regularizes the proximal subproblems and makes them well conditioned. Case in point, the subproblem may become convex despite \(f\) not being convex; and even if \(f\) were convex, the subproblem has a larger strong convexity parameter thereby facilitating faster numerical methods.
Despite the improved conditioning, each proximal subproblem still requires invoking an iterative solver. For this reason, the proximal point method has predominantly been thought of as a theoretical/conceptual algorithm, only guiding algorithm design and analysis rather than being implemented directly. One good example is the proximal bundle method (Lemarechal, Strodiot, and Bihain 1981), which approximates each proximal subproblem by a cutting plane model. In the past few years, this viewpoint has undergone a major revision. In a variety of circumstances, the proximal point method (or a close variant) with a judicious choice of the control parameter \(\nu>0\) and an appropriate iterative method for the subproblems can lead to practical and theoretically sound numerical methods. In this blog, I will briefly describe three recent examples of this trend:

Episode 1: a subgradient method for weakly convex stochastic approximation problems (Davis and Grimmer 2017),

Episode 2: the proxlinear algorithm for minimizing compositions of convex functions and smooth maps (Drusvyatskiy and Lewis 2016; Drusvyatskiy and Paquette 2016; Burke and Ferris 1995; Nesterov 2007; Lewis and Wright 2015; Cartis, Gould, and Toint 2011),

Episode 3: Catalyst generic acceleration schema (Lin, Mairal, and Harchaoui 2015; 2017) for regularized Empirical Risk Minimization.
Each epsiode, discussing the examples above, is selfcontained and can be read independently of the others. A version of this blog series will appear in SIAG/OPT Views and News 2018.
Notation
The following two constructions will play a basic role in the blog. For any closed function \(f\) on \({\mathbb R}^d\), the Moreau envelope and the proximal map are
\[\begin{aligned} f_{\nu}(z)&:=\inf_{x}~\left\{f(x)+\tfrac{1}{2\nu}\xz\^2\right\},\\ {\rm prox}_{\nu f}(z)&:=\underset{x}{\operatorname{argmin}}~\left\{f(x)+\tfrac{1}{2\nu}\xz\^2\right\}, \end{aligned}\]respectively. In this notation, the proximal point method is simply the fixedpoint recurrence on the proximal map:^{1}
\[{\bf Step\, }t: \qquad \textrm{choose }x_{t+1}\in {\rm prox}_{\nu f}(x_t).\]Clearly, in order to have any hope of solving the proximal subproblems, one must ensure that they are convex. Consequently, the class of weakly convex functions forms the natural setting for the proximal point method.
A function \(f\) is called \(\rho\)weakly convex if the assignment \(x\mapsto f(x)+\frac{\rho}{2}\x\^2\) is a convex function.
For example, a \(C^1\)smooth function with \(\rho\)Lipschitz gradient is \(\rho\)weakly convex, while a \(C^2\)smooth function \(f\) is \(\rho\)weakly convex precisely when the minimal eigenvalue of its Hessian is uniformly bounded below by \(\rho\). In essence, weak convexity precludes functions that have downward kinks. For instance, \(f(x):=\x\\) is not weakly convex since no addition of a quadratic makes the resulting function convex.
Whenever \(f\) is \(\rho\)weakly convex and the proximal parameter \(\nu\) satisfies \(\nu<\rho^{1}\), each proximal subproblem is itself convex and therefore globally tractable. Moreover, in this setting, the Moreau envelope is \(C^1\)smooth with the gradient
\[\nabla f_{\nu}(x)=\nu^{1}(x{\rm prox}_{\nu f}(x)).\]Rearranging the gradient formula yields the useful interpretation of the proximal point method as gradient descent on the Moreau envelope
\[x_{t+1}=x_t\nu\nabla f_{\nu}(x_t).\]In summary, the Moreau envelope \(f_{\nu}\) serves as a \(C^1\)smooth approximation of \(f\) for all small \(\nu\). Moreover, the two conditions
\[\\nabla f_{\nu}(x_{t})\< \varepsilon\]and
\[\\nu^{1}(x_tx_{t+1})\<\varepsilon,\]are equivalent for the proximal point sequence \(\{x_t\}\). Hence, the stepsize \(\x_tx_{t+1}\\) of the proximal point method serves as a convenient termination criteria.
Examples of weakly convex functions
Weakly convex functions are widespread in applications and are typically easy to recognize. One common source of weakly convex functions is the composite problem class \(\mathcal{C}\):
\[\min_{x}~ F(x):=g(x)+h(c(x)),\]where \(g\colon {\mathbb R}^d\to{\mathbb R}\cup\{+\infty\}\) is a closed convex function, \(h\colon{\mathbb R}^m\to{\mathbb R}\) is convex and \(L\)Lipschitz, and \(c\colon{\mathbb R}^d\to{\mathbb R}^m\) is a \(C^1\)smooth map with \(\beta\)Lipschitz gradient. An easy argument shows that \(F\) is \(L\beta\)weakly convex. This is a worst case estimate. In concrete circumstances, the composite function \(F\) may have a much more favorable weak convexity constant (e.g., phase retrieval (Duchi and Ruan 2017a, Section 3.2)).

(Additive composite) The most prevalent example is additive composite minimization. In this case, the map \(c\) maps to the real line and \(h\) is the identity function:
\[\label{eqn:add_comp} \min_{x}~ c(x)+g(x).\]Such problems appear often in statistical learning and imaging. A variety of specialized algorithms are available; see for example Beck and Teboulle (2012) or Nesterov (2013).

(Nonlinear least squares)
The composite problem class also captures nonlinear least squares problems with bound constraints:
\[\begin{aligned} \min_x~ \c(x)\_2\quad \textrm{subject to}\quad l_i\leq x_i\leq u_i ~\forall i. \end{aligned}\]Such problems pervade engineering and scientific applications.

(Exact penalty formulations) Consider a nonlinear optimization problem:
\[\begin{aligned} \min_x~ \{f(x): G(x)\in \mathcal{K}\}, \end{aligned}\]where \(f\) and \(G\) are smooth maps and \(\mathcal{K}\) is a closed convex cone. An accompanying penalty formulation – ubiquitous in nonlinear optimization – takes the form
\[\min_x~ f(x)+\lambda \cdot {\rm dist}_{\mathcal{K}}(G(x)),\]where \({\rm dist}_{\mathcal{K}}(\cdot)\) is the distance to \(\mathcal{K}\) in some norm. Historically, exact penalty formulations served as the early motivation for the composite class \(\mathcal{C}\).

(Robust phase retrieval) Phase retrieval is a common computational problem, with applications in diverse areas, such as imaging, Xray crystallography, and speech processing. For simplicity, I will focus on the version of the problem over the reals. The (real) phase retrieval problem seeks to determine a point \(x\) satisfying the magnitude conditions,
\[\langle a_i,x\rangle\approx b_i\quad \textrm{for }i=1,\ldots,m,\]where \(a_i\in {\mathbb R}^d\) and \(b_i\in{\mathbb R}\) are given. Whenever there are gross outliers in the measurements \(b_i\), the following robust formulation of the problem is appealing (Eldar and Mendelson 2014; Duchi and Ruan 2017a; Davis, Drusvyatskiy, and Paquette 2017):
\[\min_x ~\tfrac{1}{m}\sum_{i=1}^m \langle a_i,x\rangle^2b_i^2.\]Clearly, this is an instance of the composite class \(\mathcal{C}\). For some recent perspectives on phase retrieval, see the survey (Luke 2017). There are numerous recent nonconvex approaches to phase retrieval, which rely on alternate problem formulations; e.g., (Candès, Li, and Soltanolkotabi 2015; Chen and Candès 2017; Sun, Qu, and Wright 2017).

(Robust PCA) In robust principal component analysis, one seeks to identify sparse corruptions of a lowrank matrix (Candès et al. 2011; Chandrasekaran et al. 2011). One typical example is image deconvolution, where the lowrank structure models the background of an image while the sparse corruption models the foreground. Formally, given a \(m\times n\) matrix \(M\), the goal is to find a decomposition \(M=L+S\), where \(L\) is lowrank and \(S\) is sparse. A common formulation of the problem reads:
\[\min_{U\in {\mathbb R}^{m\times r},V\in {\mathbb R}^{n\times r}}~ \UV^TM\_1,\]where \(r\) is the target rank.

(Censored \(\mathbb{Z}_2\) synchronization) A synchronization problem over a graph is to estimate group elements \(g_1,\ldots, g_n\) from pairwise products \(g_ig_j^{1}\) over a set of edges \(ij\in E\). For a list of application of such problem see (Bandeira, Boumal, and Voroninski 2016; Singer 2011; Abbe et al. 2014), and references therein. A simple instance is \(\mathbb{Z}_2\) synchronization, corresponding to the group on two elements \(\{1,+1\}\). The popular problem of detecting communities in a network, within the Binary Stochastic Block Model (SBM), can be modeled using \(\mathbb{Z}_2\) synchronization.
Formally, given a partially observed matrix \(M\), the goal is to recover a vector \(\theta\in \{\pm 1\}^d\), satisfying \(M_{ij}\approx \theta_i \theta_j\) for all \(ij\in E\). When the entries of \(M\) are corrupted by adversarial sign flips, one can postulate the following formulation
\[\min_{\theta\in {\mathbb R}^{d}}~ \P_{E}(\theta\theta^TM)\_1,\]where the operator \(P_E\) records the entries indexed by the edge set \(E\). Clearly, this is again an instance of the composite problem class \(\mathcal{C}\).
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To ensure that \({\rm prox}_{\nu f}(\cdot)\) is nonempty, it suffices to assume that \(f\) is bounded from below. ↩