Iterative Solver
The iterative solver provides an iterative method to minimize an objective function
that can be represented as a sum of functions in composite form
where:
, 
,
where 
is a convex, continuously differentiable
(smooth) functions, i = 1, …, n
is a convex, non-differentiable (non-smooth) function
The Algorithmic Framework of an Iterative Solver
All solvers presented in the library follow a common algorithmic framework.
Let 
be a set of intrinsic parameters of the iterative solver for updating the argument of the objective function.
This set is the algorithm-specific and can be empty. The solver determines the choice of 
.
be a set of intrinsic parameters of the iterative solver for updating the argument of the objective function.
This set is the algorithm-specific and can be empty. The solver determines the choice of .To do the computations, iterate 
:
t
from 1
until :- Choose a set of indices without replacement
, 
,
j = 1, ldots, b, wherebis the batch size. - Compute the gradient
where
- Convergence check:Stop if
where Uis an algorithm-specific vector (argument or gradient) and d is an algorithm-specific power of Lebesgue space - Compute
using the algorithm-specific transformation Tthat updates the function’s argument:
- Update
where Uis an algorithm-specific update of the set of intrinsic parameters.
The result of the solver is the argument 
and a set of parameters 
after the exit from the loop.
and a set of parameters after the exit from the loop.You can resume the computations to get a more precise estimate of the objective function minimum.
To do this, pass to the algorithm the results and of the previous run of the optimization solver.
By default, the solver does not return the set of intrinsic parameters.
If you need it, set the
and of the previous run of the optimization solver.
By default, the solver does not return the set of intrinsic parameters.
If you need it, set the optionalResultRequired
flag for the algorithm.