### Popular Comparisons

## README

## rustimization

A rust optimization library which includes **L-BFGS-B** and **Conjugate Gradient** algorithm.

## Documentation

The simplest way to use these optimization algorithm is to use the Funcmin class.

```
extern crate rustimization;
use rustimization::minimizer::Funcmin;
fn test(){
let f = |x: &Vec<f64>| { (x[0]+4.0).powf(2.0)};
let g = |x: &Vec<f64>| {vec![2.0*(x[0]+4.0)]};
let mut x = vec![40.0f64];
{
//you must create a mutable object
let mut fmin = Funcmin::new(&mut x,&f,&g,"cg");
fmin.minimize();
}
println!("{:?}",x);
}
```

Output

```
[-4.000000000000021]
```

here Funcmin constructor takes four **parameters** first one is initial estimation **x** second and third one is the function **f** and
the derivative **g** of the function respectively and forth one is the algorithm you want to use. Currently two algorithms
available **"cg"** and **"lbfgsb"**
if you want more parameter tuning you can use the classes of the algorithm such as for Lbbfgsb_minimizer class

### Example

```
let f = |x:&Vec<f64>|{ (x[0]+4.0).powf(2.0)};
let g = |x:&Vec<f64>|{vec![2.0*(x[0]+4.0)]};
let mut x = vec![40.0f64];
{
//creating lbfgsb object. here it takes three parameter
let mut fmin = Lbfgsb::new(&mut x,&f,&g);
//seting upper and lower bound first parameter is the index and second one is value
fmin.set_upper_bound(0,100.0);
fmin.set_lower_bound(0,10.0);
//set verbosity. higher value is more verbosity. the value is -1<= to <=101
fmin.set_verbosity(101);
//start the algorithm
fmin.minimize();
}
println!("{:?}",x);
```

Output

```
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 1 M = 5
L = 1.0000D+01
X0 = 4.0000D+01
U = 1.0000D+02
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.93600D+03 |proj g|= 3.00000D+01
ITERATION 1
---------------- CAUCHY entered-------------------
There are 1 breakpoints
Piece 1 --f1, f2 at start point -7.7440D+03 7.7440D+03
Distance to the next break point = 3.4091D-01
Distance to the stationary point = 1.0000D+00
Variable 1 is fixed.
Cauchy X =
1.0000D+01
---------------- exit CAUCHY----------------------
0 variables are free at GCP 1
LINE SEARCH 0 times; norm of step = 30.000000000000000
At iterate 1 f= 1.96000D+02 |proj g|= 0.00000D+00
X = 1.0000D+01
G = 2.8000D+01
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
1 1 2 1 0 1 0.000D+00 1.960D+02
X = 1.0000D+01
F = 196.00000000000000
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
Cauchy time 1.570E-04 seconds.
Subspace minimization time 0.000E+00 seconds.
Line search time 1.800E-05 seconds.
Total User time 9.330E-04 seconds.
convergence!
```

## Requirements

To use this library you must have **gfortran** installed in your pc

- for
**windows**use fortran compiler provided by mingw or TDM-GCC - for
**linux**you can use the package manager to install gfortran - for Mac os you can install it form here or here

The orginal **L-BFGS-B** fortran subroutine is distributed under BSD-3 license. To know more about the condition to use this fortran routine please go here.

To know more about the condition to use the **Conjugate Gradient** Fortran routine please go here

## References

- R. H. Byrd, P. Lu and J. Nocedal. A Limited Memory Algorithm for Bound Constrained Optimization, (1995), SIAM Journal on Scientific and Statistical Computing , 16, 5, pp. 1190-1208.
- C. Zhu, R. H. Byrd and J. Nocedal. L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization (1997), ACM Transactions on Mathematical Software, Vol 23, Num. 4, pp. 550 - 560.
- J.L. Morales and J. Nocedal. L-BFGS-B: Remark on Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization (2011), to appear in ACM Transactions on Mathematical Software.
- J. C. Gilbert and J. Nocedal. Global Convergence Properties of Conjugate Gradient Methods for Optimization, (1992) SIAM J. on Optimization, 2, 1.

*
*Note that all licence references and agreements mentioned in the rustimization README section above
are relevant to that project's source code only.
*