nlminb2NLP: Constrained nonlinear minimization

In EricQLi/Rnlminb2: An R extension library for constrained optimization with nlminb.

Description

Solve constrained nonlinear minimization problem with nonlinear constraints. An alternative function wrapper.

Usage

nlminb2NLP( 
    start, fun,
    par.lower = NULL, par.upper = NULL,   
    eqA = NULL, eqA.bound = NULL,
    ineqA = NULL, ineqA.lower = NULL, ineqA.upper = NULL,   
    eqFun = list(), eqFun.bound = NULL,
    ineqFun = list(), ineqFun.lower = NULL, ineqFun.upper = NULL,
    control = list())

Arguments

start	parameter vector(vector object).
fun	the objective function to be minimized. Currently, fn must take only one argument, and the parameter vector(par) will be passed to fn during the optimization. The first element of return value must be the evaluated value.
par.lower, par.upper	upper and lower bounds for parameter vector, respectively. Their length must equal to length(par).
eqA, ineqA	the matrix objects that represents linear constraints. Its columns must be equal to length(par), and its rows must be equal to the number of linear constraints.
eqA.bound	equality bounds for linear constraints, respectively. Their length must equal to the number of linear constraints.
ineqA.lower, ineqA.upper	upper and lower bounds for linear constraints, respectively. Their length must equal to the number of linear constraints.
eqFun	list object whose elements are functions that represents nonlinear equality constraints.
eqFun.bound	equality bounds for nonlinear constraints, respectively.
ineqFun	list object whose elements are functions that represents nonlinear lower and upper constraints.
ineqFun.lower, ineqFun.upper	lower and upper bounds for nonlinear constraints, respectively.
control	list of control parameters that define the behaviour of the solver. See nlminb2Control for details.

Value

A list with following elements:

opt	a list of information on the optimal solution as returned by the function donlp2.
solution	a numeric vector, the optimal solution.
objective	a numeric value, the value at the optimal solution
status	not used, returns NA.
message	a character string, the convergence message.
solver	a character string, the name of the solver.
version	a character string, the convergence message.

Author(s)

For the R port of nlminb Douglas Bates and Deepayan Sarkar, for the R/Rmetrics port of nlminb2 Diethelm Wuertz, for the PORT library netlib.bell-labs.com.

References

Paul A. Jensen & Jonathan F. Bard, Operations Research Models and Methods, 2001 Appendix A, Algorithms for Constrained Optimization, http://www.me.utexas.edu/~jensen/ORMM/supplements/index.html.

PORT Library, http://netlib.bell-labs.com/netlib/port/.

Examples

## Example:

   # Feasible Start Solution:
   start <- c(10, 10)
   
   # Objective Function: x^2 + y^2 
   fun <- function(x) sum(x^2)
   
   # Bounds: -100 <= x,y <= 100
   par.lower <- c(-100, -100)
   par.upper <- c(100, 100)
    
   # Equality Constraints: x*y = 2
   eqFun <- list(function(x) x[1]*x[2])
   eqFun.bound <- 2
  
   # Solution: x = c(sqrt(2), sqrt(2)), f(x) = 4   
   nlminb2NLP(start = start, fun = fun, 
     par.lower = par.lower, par.upper = par.upper,
     eqFun = eqFun, eqFun.bound = eqFun.bound)[-1]

EricQLi/Rnlminb2 documentation built on May 6, 2019, 4:03 p.m.