QUADPROG
========
This module contains routines for solving quadratic programming problems,
written in JavaScript.
quadprog is a porting of a R package:
quadprog, implemented in
Fortran.
It implements the dual method of Goldfarb and Idnani (1982, 1983) for solving
quadratic programming problems of the form min(d T b + 1=2b T Db) with the
constraints AT b >= b0.
References
==========
D. Goldfarb and A. Idnani (1982). Dual and Primal-Dual Methods for Solving
Strictly Convex Quadratic Programs. In J. P. Hennart (ed.), Numerical Analysis,
Springer-Verlag, Berlin, pages 226–239.
D. Goldfarb and A. Idnani (1983). A numerically stable dual method for solving
strictly convex quadratic programs. Mathematical Programming, 27, 1–33.
Example
========
```
// ##
// ## Assume we want to minimize: -(0 5 0) %

*% b + 1/2 b^T b // ## under the constraints: A^T b >= b0 // ## with b0 = (-8,2,0)^T // ## and // ## (-4 2 0) // ## A = (-3 1 -2) // ## ( 0 0 1) // ## we can use solve.QP as follows: // ## // Dmat <- matrix(0,3,3) // diag(Dmat) <- 1 // dvec <- c(0,5,0) // Amat <- matrix(c(-4,-3,0,2,1,0,0,-2,1),3,3) // bvec <- c(-8,2,0) // solve.QP(Dmat,dvec,Amat,bvec=bvec) var qp = require('quadprog'); var Dmat =*, dvec = , Amat = , bvec = , res; Dmat1 = ; Dmat2 = ; Dmat3 = ; Dmat11 = 1; Dmat21 = 0; Dmat31 = 0; Dmat12 = 0; Dmat22 = 1; Dmat32 = 0; Dmat13 = 0; Dmat23 = 0; Dmat33 = 1; dvec1 = 0; dvec2 = 5; dvec3 = 0; Amat1 = ; Amat2 = ; Amat3 = ; Amat11 = -4; Amat21 = -3; Amat31 = 0; Amat12 = 2; Amat22 = 1; Amat32 = 0; Amat13 = 0; Amat23 = -2; Amat33 = 1; bvec1 = -8; bvec2 = 2; bvec3 = 0; res = qp.solveQP(Dmat, dvec, Amat, bvec) ``` Installation ============ To install with npm:`npm install quadprog`

Tested locally with Node.js 10.x and with R 3.4.1.
Notes
=====
**To maintain a one-to-one porting with the Fortran implementation, the array index starts from 1 and not from zero. Please, be aware and give a look at the examples in the test folder**. If you are using`node-quadprog`

via Numeric.js, don't forget the releases may
be not in sync. Latest release is here.
Applications
============
See also
========
Methods
=======
solveQP(Dmat, dvec, Amat, bvec, meq=0, factorized=FALSE)
-------
**Arguments***Dmat*matrix appearing in the quadratic function to be minimized.

*dvec*vector appearing in the quadratic function to be minimized.

*Amat*matrix deﬁning the constraints under which we want to minimize the

*bvec*vector holding the values of b0 (defaults to zero).

*meq*the ﬁrst meq constraints are treated as equality constraints, all

*factorized*logical ﬂag: if TRUE, then we are passing R1 (where D = RT R)

**Value**An object with the following property:*solution*vector containing the solution of the quadratic programming

*value*scalar, the value of the quadratic function at the solution

*unconstrained.solution*vector containing the unconstrained minimizer of the

*iterations*vector of length 2, the ﬁrst component contains the number of

*Lagrangian*vector with the Lagrangian multipliers at the solution.

*iact*vector with the indices of the active constraints at the solution.

*message*string containing an error message, if the call failed, otherwise empty.

`<name>-data.json`

.
These can be passed into `solve.R`

to create the standard R results for solveQP with the name `<name>-result.json`

.
The standard usage is `Rscript solve.R *-data.json`

, but you may wish to only create result files for specific tests.
The combination of these files is then used by `solution-test.js`

and `bench.js`

.
Adding Tests
------------
To add a new test simply create a file called `<name>-data.json`

in the test directory, and then call `Rscript solve.R <name>-data.json`

and commit the results.