# Fixed points¤

####
`optimistix.fixed_point(fn: Union[Callable[[~Y, Any], tuple[~Y, ~Aux]], Callable[[~Y, Any], ~Y]], solver: Union[AbstractFixedPointSolver, AbstractRootFinder, AbstractLeastSquaresSolver, AbstractMinimiser], y0: ~Y, args: PyTree[Any] = None, options: Optional[dict[str, Any]] = None, *, has_aux: bool = False, max_steps: Optional[int] = 256, adjoint: AbstractAdjoint = ImplicitAdjoint(linear_solver=AutoLinearSolver(well_posed=None)), throw: bool = True, tags: frozenset[object] = frozenset()) -> Solution[~Y, ~Aux]`

¤

Find a fixed-point of a function.

Given a nonlinear function `fn(y, args)`

which returns a pytree of arrays of the
same shape as `y`

, this returns the value `z`

such that `fn(z, args) = z`

.

**Arguments:**

`fn`

: The function to find the fixed-point of. This should take two arguments`fn(y, args)`

, and return a pytree of arrays of the same shape as the input`y`

.`solver`

: The root-finder to use. This can be either an`optimistix.AbstractFixedPointSolver`

or`optimistix.AbstractRootFinder`

, or`optimistix.AbstractLeastSquaresSolver`

, or`optimistix.AbstractMinimiser`

. If`solver`

is a root-finder then it will will attempt to find the root of`fn(y, args) - y`

. If`solver`

is a least-squares or minimisation algorithm, then it will attempt to minimise`sum((fn(y, args) - y)^2)`

.`y0`

: An initial guess for what`y`

may be. Used to start the iterative process of finding the fixed point; using good initial guesses is often important.`args`

: Passed as the`args`

of`fn(y, args)`

.`options`

: Individual solvers may accept additional runtime arguments. See each individual solver's documentation for more details.`has_aux`

: If`True`

, then`fn`

may return a pair, where the first element is its function value, and the second is just auxiliary data. Keyword only argument.`max_steps`

: The maximum number of steps the solver can take. Keyword only argument.`adjoint`

: The adjoint method used to compute gradients through the fixed-point solve. Keyword only argument.`throw`

: How to report any failures. (E.g. an iterative solver running out of steps, or encountering divergent iterates.) If`True`

then a failure will raise an error. If`False`

then the returned solution object will have a`result`

field indicating whether any failures occured. (See`optimistix.Solution`

.) Keyword only argument.`tags`

: Lineax tags describing the any structure of the Jacobian of`y -> fn(y, args) - y`

with respect to y. (That is, the structure of the matrix`dfn/dy - I`

.) Used with`optimistix.ImplicitAdjoint`

to implement the implicit function theorem as efficiently as possible. Keyword only argument.

**Returns:**

An `optimistix.Solution`

object.

`optimistix.fixed_point`

supports any of the following fixed-point solvers.

Info

In addition to the solvers listed here, any root finder may also be used as the `solver`

. This is because finding the fixed point `x`

for which `f(x) = x`

, can also be accomplished by finding the root `x`

for which `f(x) - x = 0`

. If you pass in a root finder, then Optimistix will automatically rewrite your problem to treat it in this way.

Likewise, any least squares solver or minimiser may also be used as the `solver`

. This is because finding the root `x`

for which `f(x) = x`

can also be accomplished by finding the value `x`

for which `sum((f(x) - x)^2)`

is minimised. If you pass in a least squares solver or minimiser, then Optimistix will automatically rewrite your problem to treat it in this way.

`optimistix.AbstractFixedPointSolver`

####
```
optimistix.AbstractFixedPointSolver
```

¤

Abstract base class for all fixed point solvers.

#####
`init(self, fn: Callable[[~Y, Any], tuple[~Out, ~Aux]], y: ~Y, args: PyTree, options: dict[str, Any], f_struct: PyTree[jax.ShapeDtypeStruct], aux_struct: PyTree[jax.ShapeDtypeStruct], tags: frozenset[object]) -> ~SolverState`

`abstractmethod`

¤

Perform all initial computation needed to initialise the solver state.

For example, the `optimistix.Chord`

method computes the Jacobian `df/dy`

with respect to the initial guess `y`

, and then uses it throughout the
computation.

**Arguments:**

`fn`

: The function to iterate over. This is expected to take two argumetns`fn(y, args)`

and return a pytree of arrays in the first element, and any auxiliary data in the second argument.`y`

: The value of`y`

at the current (first) iteration.`args`

: Passed as the`args`

of`fn(y, args)`

.`options`

: Individual solvers may accept additional runtime arguments. See each individual solver's documentation for more details.`f_struct`

: A pytree of`jax.ShapeDtypeStruct`

s of the same shape as the output of`fn`

. This is used to initialise any information in the state which may rely on the pytree structure, array shapes, or dtype of the output of`fn`

.`aux_struct`

: A pytree of`jax.ShapeDtypeStruct`

s of the same shape as the auxiliary data returned by`fn`

.`tags`

: exact meaning depends on whether this is a fixed point, root find, least squares, or minimisation problem; see their relevant entry points.

**Returns:**

A PyTree representing the initial state of the solver.

#####
`step(self, fn: Callable[[~Y, Any], tuple[~Out, ~Aux]], y: ~Y, args: PyTree, options: dict[str, Any], state: ~SolverState, tags: frozenset[object]) -> tuple[~Y, ~SolverState, ~Aux]`

`abstractmethod`

¤

Perform one step of the iterative solve.

**Arguments:**

`fn`

: The function to iterate over. This is expected to take two argumetns`fn(y, args)`

and return a pytree of arrays in the first element, and any auxiliary data in the second argument.`y`

: The value of`y`

at the current (first) iteration.`args`

: Passed as the`args`

of`fn(y, args)`

.`options`

: Individual solvers may accept additional runtime arguments. See each individual solver's documentation for more details.`state`

: A pytree representing the state of a solver. The shape of this pytree is solver-dependent.`tags`

: exact meaning depends on whether this is a fixed point, root find, least squares, or minimisation problem; see their relevant entry points.

**Returns:**

A 3-tuple containing the new `y`

value in the first element, the next solver
state in the second element, and the aux output of `fn(y, args)`

in the third
element.

#####
`terminate(self, fn: Callable[[~Y, Any], tuple[~Out, ~Aux]], y: ~Y, args: PyTree, options: dict[str, Any], state: ~SolverState, tags: frozenset[object]) -> tuple[Array, RESULTS]`

`abstractmethod`

¤

Determine whether or not to stop the iterative solve.

**Arguments:**

`fn`

: The function to iterate over. This is expected to take two argumetns`fn(y, args)`

and return a pytree of arrays in the first element, and any auxiliary data in the second argument.`y`

: The value of`y`

at the current iteration.`args`

: Passed as the`args`

of`fn(y, args)`

.`options`

: Individual solvers may accept additional runtime arguments. See each individual solver's documentation for more details.`state`

: A pytree representing the state of a solver. The shape of this pytree is solver-dependent.`tags`

: exact meaning depends on whether this is a fixed point, root find, least squares, or minimisation problem; see their relevant entry points.

**Returns:**

A 2-tuple containing a bool indicating whether or not to stop iterating in the
first element, and an `optimistix.RESULTS`

object in the second element.

#####
`postprocess(self, fn: Callable[[~Y, Any], tuple[~Out, ~Aux]], y: ~Y, aux: ~Aux, args: PyTree, options: dict[str, Any], state: ~SolverState, tags: frozenset[object], result: RESULTS) -> tuple[~Y, ~Aux, dict[str, Any]]`

`abstractmethod`

¤

Any final postprocessing to perform on the result of the solve.

**Arguments:**

`fn`

: The function to iterate over. This is expected to take two argumetns`fn(y, args)`

and return a pytree of arrays in the first element, and any auxiliary data in the second argument.`y`

: The value of`y`

at the last iteration.`aux`

: The auxiliary output at the last iteration.`args`

: Passed as the`args`

of`fn(y, args)`

.`options`

: Individual solvers may accept additional runtime arguments. See each individual solver's documentation for more details.`state`

: A pytree representing the final state of a solver. The shape of this pytree is solver-dependent.`tags`

: exact meaning depends on whether this is a fixed point, root find, least squares, or minimisation problem; see their relevant entry points.`result`

: as returned by the final call to`terminate`

.

**Returns:**

A 3-tuple of:

`final_y`

: the final`y`

to return as the solution of the solve.`final_aux`

: the final`aux`

to return as the auxiliary output of the solve.`stats`

: any additional information to place in the`sol.stats`

dictionary.

Info

Most solvers will not need to use this, so that this method may be defined as:

```
def postprocess(self, fn, y, aux, args, options, state, tags, result):
return y, aux, {}
```

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####
```
optimistix.FixedPointIteration (AbstractFixedPointSolver)
```

¤

Repeatedly calls a function in search of a fixed point.

This is one of the simplest ways to find a fixed point `y`

of `f`

: simply
repeatedly call `y_{n+1}=f(y_n)`

until `y_n`

stops changing.

Note that this is often not a very effective method, and root-finding algorithms are frequently preferred in practice.

#####
`__init__(self, rtol: float, atol: float, norm: Callable[[PyTree], Shaped[Array, '']] = <function max_norm>)`

¤

####
```
optimistix.BestSoFarFixedPoint (AbstractFixedPointSolver)
```

¤

Wraps another fixed-point solver, to return the best-so-far value. That is, it
makes a copy of the best `y`

seen, and returns that.