# Solving Nonlinear System of Equations - A Wrapper for dfsane()

### Description

A strategy using different Barzilai-Borwein steplengths to solve a nonlinear system of equations.

### Usage

1 2 3 |

### Arguments

`par` |
A real vector argument to |

`fn` |
Nonlinear system of equation that is to be solved. A vector function that takes a real vector as argument and returns a real vector of the same length. |

`method` |
A vector of integers specifying which Barzilai-Borwein steplengths should be used in a consecutive manner. The methods will be used in the order specified. |

`control` |
A list of parameters governing the algorithm behaviour.
This list is the same as that for |

`quiet` |
logical indicating if messages about convergence success or failure should be suppressed |

`...` |
arguments passed fn (via the optimization algorithm). |

### Details

This wrapper is especially useful in problems where the algorithms
(`dfsane`

or `sane`

) are likely to experience difficulties in
convergence. When these algorithms with default parameters fail, i.e.
when `convergence > 0`

is obtained, a user might attempt various
strategies to find a root of the nonlinear system. The function `BBsolve`

tries the following sequential strategy:

Try a different BB steplength. Since the default is

`method = 2`

for`dfsane`

, the BBsolve wrapper tries`method = c(2, 1, 3)`

.Try a different non-monotonicity parameter

`M`

for each method, i.e. BBsolve wrapper tries`M = c(50, 10)`

for each BB steplength.Try with Nelder-Mead initialization. Since the default for

`dfsane`

is`NM = FALSE`

, BBsolve does`NM = c(TRUE, FALSE)`

.

The argument `control`

defaults to a list with values
```
maxit = 1500, M = c(50, 10), tol = 1e-07, trace = FALSE,
triter = 10, noimp = 100, NM = c(TRUE, FALSE)
```

.
If `control`

is specified as an argument, only values which are different
need to be given in the list. See `dfsane`

for more details.

### Value

A list with the same elements as returned by `dfsane`

or `sane`

. One additional element returned is `cpar`

which
contains the control parameter settings used to obtain successful
convergence, or to obtain the best solution in case of failure.

### References

R Varadhan and PD Gilbert (2009), BB: An R Package for Solving a Large System of Nonlinear Equations and for Optimizing a High-Dimensional Nonlinear Objective Function, *J. Statistical Software*, 32:4, http://www.jstatsoft.org/v32/i04/

### See Also

`BBoptim`

,
`dfsane`

,
`sane`

`multiStart`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
# Use a preset seed so test values are reproducable.
require("setRNG")
old.seed <- setRNG(list(kind="Mersenne-Twister", normal.kind="Inversion",
seed=1234))
broydt <- function(x) {
n <- length(x)
f <- rep(NA, n)
h <- 2
f[1] <- ((3 - h*x[1]) * x[1]) - 2*x[2] + 1
tnm1 <- 2:(n-1)
f[tnm1] <- ((3 - h*x[tnm1]) * x[tnm1]) - x[tnm1-1] - 2*x[tnm1+1] + 1
f[n] <- ((3 - h*x[n]) * x[n]) - x[n-1] + 1
f
}
p0 <- rnorm(50)
BBsolve(par=p0, fn=broydt) # this works
dfsane(par=p0, fn=broydt) # but this is highly unliikely to work.
# this implements the 3 BB steplengths with M = 50, and without Nelder-Mead initialization
BBsolve(par=p0, fn=broydt, control=list(M=50, NM=FALSE))
# this implements BB steplength 1 with M = 50 and 10, and both with and
# without Nelder-Mead initialization
BBsolve(par=p0, fn=broydt, method=1, control=list(M=c(50, 10)))
# identical to dfsane() with defaults
BBsolve(par=p0, fn=broydt, method=2, control=list(M=10, NM=FALSE))
``` |

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