This work introduces a _partitioned optimization framework_ (POf) to ease the solving process for optimization problems for which fixing some variables to a tunable value removes some difficulties.
The variables space may be continuously partitioned into subsets where these variables are fixed to a given value, so that minimizing the objective function restricted to any of the partition sets is easier than minimizing the objective function over the whole variables space.
Then the major difficulty is translated from solving the original problem to choosing the partition set minimizing the minimum of the objective function restricted to this set.
Hence, a local solution to the original problem is given by computing this partition set and selecting the minimizer of the objective function restricted to this set.
This work formalizes this framework, studies its theoretical guarantees, and provides a numerical method to seek for an optimal partition set using a derivative-free algorithm.
Some illustrative problems are presented to show how to apply the POf and to highlight the gain in numerical performance it provides.