# Function information¤

####
```
optimistix.FunctionInfo
```

¤

Different solvers (BFGS, Levenberg--Marquardt, ...) evaluate different quantities of the objective function. Some may compute gradient information, some may provide approximate Hessian information, etc.

This enumeration-ish object captures the different variants.

Available variants are
`optimistix.FunctionInfo.{Eval, EvalGrad, EvalGradHessian, EvalGradHessianInv, Residual, ResidualJac}`

.

#####
`as_min(self) -> Array`

`abstractmethod`

¤

For a minimisation problem, returns f(y). For a least-squares problem, returns 0.5*residuals^2 -- i.e. its loss as a minimisation problem.

####
```
optimistix.FunctionInfo.Eval (FunctionInfo)
```

¤

Has a `.f`

attribute describing `fn(y)`

. Used when no gradient information is
available.

####
```
optimistix.FunctionInfo.EvalGrad (FunctionInfo)
```

¤

Has a `.f`

attribute as with `optimistix.FunctionInfo.Eval`

. Also has a
`.grad`

attribute describing `d(fn)/dy`

. Used with first-order solvers for
minimisation problems. (E.g. gradient descent; nonlinear CG.)

####
```
optimistix.FunctionInfo.EvalGradHessian (FunctionInfo)
```

¤

Has `.f`

and `.grad`

attributes as with `optimistix.FunctionInfo.EvalGrad`

.
Also has a `.hessian`

attribute describing (an approximation to) the Hessian of
`fn`

at `y`

. Used with quasi-Newton minimisation algorithms, like BFGS.

####
```
optimistix.FunctionInfo.EvalGradHessianInv (FunctionInfo)
```

¤

As `optimistix.FunctionInfo.EvalGradHessian`

, but records the (approximate)
inverse-Hessian instead. Has `.f`

and `.grad`

and `.hessian_inv`

attributes.

####
```
optimistix.FunctionInfo.Residual (FunctionInfo)
```

¤

Has a `.residual`

attribute describing `fn(y)`

. Used with least squares problems,
for which `fn`

returns residuals.

####
```
optimistix.FunctionInfo.ResidualJac (FunctionInfo)
```

¤

Records the Jacobian `d(fn)/dy`

as a linear operator. Used for least squares
problems, for which `fn`

returns residuals. Has `.residual`

and `.jac`

and `.grad`

attributes, where `residual = fn(y)`

, `jac = d(fn)/dy`

and
`grad = jac^T residual`

.

Takes just `residual`

and `jac`

as `__init__`

-time arguments, from which `grad`

is
computed.