Time-Varying Convex Optimization via Time-Varying Averaged Operators

Understand

Devising efficient algorithms that track the optimizers of continuously varying convex optimization problems is key in many applications.

• A possible strategy is to sample the time-varying problem at constant rate and solve the resulting time-invariant problem.
• This can be too computationally burdensome in many scenarios.
• An alternative strategy is to set up an iterative algorithm that generates a sequence of approximate optimizers, which are refined every time a new sampled time-invariant problem is available by one iteration of the algorithm.