### 6 releases (3 breaking)

✓ Uses Rust 2018 edition

0.4.2 | Feb 17, 2019 |
---|---|

0.4.1 | Feb 14, 2019 |

0.3.0 | Jan 28, 2019 |

0.2.0 | Jan 27, 2019 |

0.1.0 | Jan 19, 2019 |

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# totsu

Totsu (凸 in Japanese) means convex.

This crate for Rust provides a basic **primal-dual interior-point method** solver:

.`PDIPM`

## Target problem

A common target problem is continuous scalar **convex optimization** such as
LP, QP and QCQP. SOCP and SDP can also be handled with a certain effort.

## Algorithm and design concepts

The overall algorithm is based on the reference:
*S. Boyd and L. Vandenberghe, "Convex Optimization",*
http://stanford.edu/~boyd/cvxbook/.

has a core method `PDIPM`

which takes objective and constraint (derivative) functions as closures.
Therefore solving a specific problem requires an implementation of those closures.
You can use a pre-defined implementations (see `solve`

),
as well as construct a user-defined tailored version for the reason of functionality and efficiency.`predef`

This crate has no dependencies on other crates at all.
Necessary matrix operations are implemented in

and `mat`

.`matsvd`

## Examples

### QP

`use` `totsu``::``prelude``::``*``;`
`use` `totsu``::``predef``::``*``;`
`let` n`:` `usize` `=` `2``;` `//` x0, x1
`let` m`:` `usize` `=` `1``;`
`let` p`:` `usize` `=` `0``;`
`//` (1/2)(x - a)^2 + const
`let` mat_p `=` `Mat``::`new`(`n`,` n`)``.``set_iter``(``&``[`
`1.``,` `0.``,`
`0.``,` `1.`
`]``)``;`
`let` vec_q `=` `Mat``::`new_vec`(`n`)``.``set_iter``(``&``[`
`-``(``-``1.``)``,` `//` -a0
`-``(``-``2.``)` `//` -a1
`]``)``;`
`//` 1 - x0/b0 - x1/b1 <= 0
`let` mat_g `=` `Mat``::`new`(`m`,` n`)``.``set_iter``(``&``[`
`-``1.` `/` `2.``,` `//` -1/b0
`-``1.` `/` `3.` `//` -1/b1
`]``)``;`
`let` vec_h `=` `Mat``::`new_vec`(`m`)``.``set_iter``(``&``[`
`-``1.`
`]``)``;`
`let` mat_a `=` `Mat``::`new`(`p`,` n`)``;`
`let` vec_b `=` `Mat``::`new_vec`(`p`)``;`
`let` param `=` `PDIPMParam``::`default`(``)``;`
`let` rslt `=` `PDIPM``::`new`(``)``.``solve_qp``(``&`param`,` `&``mut` `std``::``io``::`sink`(``)``,`
`&`mat_p`,` `&`vec_q`,`
`&`mat_g`,` `&`vec_h`,`
`&`mat_a`,` `&`vec_b`)``.``unwrap``(``)``;`
`let` exp `=` `Mat``::`new_vec`(`n`)``.``set_iter``(``&``[`
`2.``,` `0.`
`]``)``;`
`println!``(``"`rslt = `{}``"``,` rslt`)``;`
`assert!``(``(``&`rslt `-` exp`)``.``norm_p2``(``)` `<` param`.`eps`)``;`

### Other Examples

You can find other test examples of pre-defined solvers in

.
More practical examples are available here.`lib .rs`