# mize 0.2.1

A patch release to fix an incompatibility with R-devel.

## Bug fixes

- Fixed an error with bold driver and back-tracking line search where the stage was being incorrectly checked.

# mize 0.2.0

## New features

- New method: Truncated Newton (
`method = "TN"`

). Can be controlled using the `tn_init`

and `tn_exit`

options.
- New method: SR1 (
`method = "SR1"`

), falling back to the BFGS direction if a descent direction is not found.
- New option
`preconditioner`

, which applies to the conjugate gradient and truncated newton methods. The only value currently available is `preconditioner = "L-BFGS"`

which uses L-BFGS to estimate the inverse Hessian for preconditioning. The number of updates to store for this preconditioner is controlled by the `memory`

parameter, just as if you were using `method = "L-BFGS"`

.
- BFGS, SR1, L-BFGS methods will now make use of a user-supplied inverse Hessian function if provided. In the input
`fg`

list, supply a function `hi`

, that takes the `par`

vector as input. The function can return a matrix (obviously not a great idea for memory use), or a vector, the latter of which is assumed to be the diagonal of the matrix.
`ls_max_alpha`

(for `line_search = "More-Thuente"`

only): sets maximum value of alpha that can be attained during line search.
`ls_max_alpha_mult`

(for Wolfe-type line search only): sets maximum value that can be attained by the ratio of the initial guess for alpha for the current line search, to the final value of alpha of the previous line search. Used to stop line searches diverging due to very large initial guesses.
`ls_safe_cubic`

(for `line_search = "More-Thuente"`

only): if `TRUE`

, use the safe-guarded cubic modification suggested by Xie and Schlick.
`cg_update = "prfr"`

, the “PR-FR” (Polak-Ribiere/Fletcher-Reeves) conjugate gradient update suggested by Gilbert and Nocedal.

## Bug fixes

- Error occurred when checking if a step size was finite during line search.
- DBD method didn’t use momentum when asked to.
- Fix incorrectly specified conjugate gradient descent methods: Hestenes-Steifel (
`cg_udpate = "hs"`

), Conjugate Descent (`cg_udpate = "cd"`

), Dai-Yuan (`cg_udpate = "dy"`

) and Liu-Storey (`cg_udpate = "ls"`

).

# mize 0.1.1

Initial release.