# Cyclic Proximal Point

The Cyclic Proximal Point (CPP) algorithm is a Proximal Problem.

It aims to minimize

$F(x) = \sum_{i=1}^c f_i(x)$

assuming that the proximal maps $\operatorname{prox}_{\lambda f_i}(x)$ are given in closed form or can be computed efficiently (at least approximately).

The algorithm then cycles through these proximal maps, where the type of cycle might differ and the proximal parameter $\lambda_k$ changes after each cycle $k$.

For a convergence result on Hadamard manifolds see [Bačák, 2014].

Manopt.cyclic_proximal_pointFunction
cyclic_proximal_point(M, F, proxes, x)

perform a cyclic proximal point algorithm.

Input

• M – a manifold $\mathcal M$
• F – a cost function $F\colon\mathcal M\to\mathbb R$ to minimize
• proxes – an Array of proximal maps (Functions) (λ,x) -> y for the summands of $F$
• x – an initial value $x ∈ \mathcal M$

Optional

the default values are given in brackets

and the ones that are passed to decorate_options for decorators.

Output

• xOpt – the resulting (approximately critical) point of gradientDescent

OR

• options - the options returned by the solver (see return_options)
source
Manopt.CyclicProximalPointOptionsType
CyclicProximalPointOptions <: Options

stores options for the cyclic_proximal_point algorithm. These are the

Fields

• x0 – an point to start
• stopping_criterion – a function @(iter,x,xnew,λ_k) based on the current iter, x and xnew as well as the current value of λ.
• λ – (@(iter) -> 1/iter) a function for the values of λ_k per iteration/cycle
• evaluationOrder – (LinearEvalOrder()) how to cycle through the proximal maps. Other values are RandomEvalOrder() that takes a new random order each iteration, and FixedRandomEvalOrder() that fixes a random cycle for all iterations.

cyclic_proximal_point