A new primal-dual algorithm for minimizing the sum of three functions with a linear operator
This work addresses optimization challenges in machine learning and signal processing by providing an incremental improvement to primal-dual algorithms for specific convex problems.
The paper tackles the problem of minimizing the sum of three convex functions with a linear operator by proposing a new primal-dual algorithm, which extends existing methods, ensures convergence with broader parameter ranges, and reduces per-iteration costs, as demonstrated in numerical experiments.
In this paper, we propose a new primal-dual algorithm for minimizing $f(x) + g(x) + h(Ax)$, where $f$, $g$, and $h$ are proper lower semi-continuous convex functions, $f$ is differentiable with a Lipschitz continuous gradient, and $A$ is a bounded linear operator. The proposed algorithm has some famous primal-dual algorithms for minimizing the sum of two functions as special cases. E.g., it reduces to the Chambolle-Pock algorithm when $f = 0$ and the proximal alternating predictor-corrector when $g = 0$. For the general convex case, we prove the convergence of this new algorithm in terms of the distance to a fixed point by showing that the iteration is a nonexpansive operator. In addition, we prove the $O(1/k)$ ergodic convergence rate in the primal-dual gap. With additional assumptions, we derive the linear convergence rate in terms of the distance to the fixed point. Comparing to other primal-dual algorithms for solving the same problem, this algorithm extends the range of acceptable parameters to ensure its convergence and has a smaller per-iteration cost. The numerical experiments show the efficiency of this algorithm.