# (L-)BFGS

This page contains information about BFGS and its limited memory version L-BFGS.

## Constructors

```
BFGS(; alphaguess = LineSearches.InitialStatic(),
linesearch = LineSearches.HagerZhang(),
initial_invH = nothing,
initial_stepnorm = nothing,
manifold = Flat())
```

`initial_invH`

has a default value of `nothing`

. If the user has a specific initial matrix they want to supply, it should be supplied as a function of an array similar to the initial point `x0`

.

If `initial_stepnorm`

is set to a number `z`

, the initial matrix will be the identity matrix scaled by `z`

times the sup-norm of the gradient at the initial point `x0`

.

```
LBFGS(; m = 10,
alphaguess = LineSearches.InitialStatic(),
linesearch = LineSearches.HagerZhang(),
P = nothing,
precondprep = (P, x) -> nothing,
manifold = Flat(),
scaleinvH0::Bool = true && (typeof(P) <: Nothing))
```

## Description

This means that it takes steps according to

where $P$ is a positive definite matrix. If $P$ is the Hessian, we get Newton's method. In (L-)BFGS, the matrix is an approximation to the Hessian built using differences in the gradient across iterations. As long as the initial matrix is positive definite it is possible to show that all the follow matrices will be as well. The starting matrix could simply be the identity matrix, such that the first step is identical to the Gradient Descent algorithm, or even the actual Hessian.

There are two versions of BFGS in the package: BFGS, and L-BFGS. The latter is different from the former because it doesn't use a complete history of the iterative procedure to construct $P$, but rather only the latest $m$ steps. It doesn't actually build the Hessian approximation matrix either, but computes the direction directly. This makes more suitable for large scale problems, as the memory requirement to store the relevant vectors will grow quickly in large problems.

As with the other quasi-Newton solvers in this package, a scalar $\alpha$ is introduced as follows

and is chosen by a linesearch algorithm such that each step gives sufficient descent.

## Example

## References

Wright, Stephen, and Jorge Nocedal (2006) "Numerical optimization." Springer