When the objective function is non-smooth or has many local minima, it is hard to judge the optimality of the solution, and this usually depends critically on the starting parameters. This function enables the generation of a set of randomly chosen parameters from which to initialize multiple restarts of the solver (see note for details).

1 2 3 4 |

`pars` |
The starting parameter vector. This is not required unless the fixed option is also used. |

`fixed` |
The numeric index which indicates those parameters which should stay fixed instead of being randomly generated. |

`fun` |
The main function which takes as first argument the parameter vector and returns a single value. |

`eqfun` |
(Optional) The equality constraint function returning the vector of evaluated equality constraints. |

`eqB` |
(Optional) The equality constraints. |

`ineqfun` |
(Optional) The inequality constraint function returning the vector of evaluated inequality constraints. |

`ineqLB` |
(Optional) The lower bound of the inequality constraints. |

`ineqUB` |
(Optional) The upper bound of the inequality constraints. |

`LB` |
The lower bound on the parameters. This is not optional in this function. |

`UB` |
The upper bound on the parameters. This is not optional in this function. |

`control` |
(Optional) The control list of optimization parameters. The |

`distr` |
A numeric vector of length equal to the number of parameters, indicating the choice of distribution to use for the random parameter generation. Choices are uniform (1), truncated normal (2), and normal (3). |

`distr.opt` |
If any choice in |

`n.restarts` |
The number of solver restarts required. |

`n.sim` |
The number of random parameters to generate for every restart of the solver. Note that there will always be significant rejections if inequality bounds are present. Also, this choice should also be motivated by the width of the upper and lower bounds. |

`cluster` |
If you want to make use of parallel functionality, initialize and pass a cluster object from the parallel package (see details), and remember to terminate it! |

`rseed` |
(Optional) A seed to initiate the random number generator, else system time will be used. |

`...` |
(Optional) Additional parameters passed to the main, equality or inequality functions |

Given a set of lower and upper bounds, the function generates, for those
parameters not set as fixed, random values from one of the 3 chosen
distributions. Depending on the `eval.type`

option of the `control`

argument, the function is either directly evaluated for those points not
violating any inequality constraints, or indirectly via a penalty barrier
function jointly comprising the objective and constraints. The resulting values
are then sorted, and the best N (N = random.restart) parameter vectors
(corresponding to the best N objective function values) chosen in order to
initialize the solver. Since version 1.14, it is up to the user to prepare and
pass a cluster object from the parallel package for use with gosolnp, after
which the parLapply function is used. If your function makes use of additional
packages, or functions, then make sure to export them via the `clusterExport`

function of the parallel package. Additional arguments passed to the solver via the
... option are evaluated and exported by gosolnp to the cluster.

A list containing the following values:

`pars` |
Optimal Parameters. |

`convergence ` |
Indicates whether the solver has converged (0) or not (1). |

`values` |
Vector of function values during optimization with last one the value at the optimal. |

`lagrange` |
The vector of Lagrange multipliers. |

`hessian` |
The Hessian at the optimal solution. |

`ineqx0` |
The estimated optimal inequality vector of slack variables used for transforming the inequality into an equality constraint. |

`nfuneval` |
The number of function evaluations. |

`elapsed` |
Time taken to compute solution. |

`start.pars` |
The parameter vector used to start the solver |

The choice of which distribution to use for randomly sampling the parameter
space should be driven by the user's knowledge of the problem and confidence or
lack thereof of the parameter distribution. The uniform distribution indicates
a lack of confidence in the location or dispersion of the parameter, while the
truncated normal indicates a more confident choice in both the location and
dispersion. On the other hand, the normal indicates perhaps a lack of knowledge
in the upper or lower bounds, but some confidence in the location and dispersion
of the parameter. In using choices (2) and (3) for `distr`

,
the `distr.opt`

list must be supplied with `mean`

and `sd`

as
subcomponents for those parameters not using the uniform (the examples section
hopefully clarifies the usage).

Alexios Ghalanos and Stefan Theussl

Y.Ye (original matlab version of solnp)

Y.Ye, *Interior algorithms for linear, quadratic, and linearly constrained
non linear programming*, PhD Thesis, Department of EES Stanford University,
Stanford CA.

Hu, X. and Shonkwiler, R. and Spruill, M.C. *Random Restarts in Global
Optimization*, 1994, Georgia Institute of technology, Atlanta.

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 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | ```
## Not run:
# [Example 1]
# Distributions of Electrons on a Sphere Problem:
# Given n electrons, find the equilibrium state distribution (of minimal Coulomb
# potential) of the electrons positioned on a conducting sphere. This model is
# from the COPS benchmarking suite. See http://www-unix.mcs.anl.gov/~more/cops/.
gofn = function(dat, n)
{
x = dat[1:n]
y = dat[(n+1):(2*n)]
z = dat[(2*n+1):(3*n)]
ii = matrix(1:n, ncol = n, nrow = n, byrow = TRUE)
jj = matrix(1:n, ncol = n, nrow = n)
ij = which(ii<jj, arr.ind = TRUE)
i = ij[,1]
j = ij[,2]
# Coulomb potential
potential = sum(1.0/sqrt((x[i]-x[j])^2 + (y[i]-y[j])^2 + (z[i]-z[j])^2))
potential
}
goeqfn = function(dat, n)
{
x = dat[1:n]
y = dat[(n+1):(2*n)]
z = dat[(2*n+1):(3*n)]
apply(cbind(x^2, y^2, z^2), 1, "sum")
}
n = 25
LB = rep(-1, 3*n)
UB = rep(1, 3*n)
eqB = rep(1, n)
ans = gosolnp(pars = NULL, fixed = NULL, fun = gofn, eqfun = goeqfn, eqB = eqB,
LB = LB, UB = UB, control = list(outer.iter = 100, trace = 1),
distr = rep(1, length(LB)), distr.opt = list(), n.restarts = 2, n.sim = 20000,
rseed = 443, n = 25)
# should get a function value around 243.813
# [Example 2]
# Parallel functionality for solving the Upper to Lower CVaR problem (not properly
# formulated...for illustration purposes only).
mu =c(1.607464e-04, 1.686867e-04, 3.057877e-04, 1.149289e-04, 7.956294e-05)
sigma = c(0.02307198,0.02307127,0.01953382,0.02414608,0.02736053)
R = matrix(c(1, 0.408, 0.356, 0.347, 0.378, 0.408, 1, 0.385, 0.565, 0.578, 0.356,
0.385, 1, 0.315, 0.332, 0.347, 0.565, 0.315, 1, 0.662, 0.378, 0.578,
0.332, 0.662, 1), 5,5, byrow=TRUE)
# Generate Random deviates from the multivariate Student distribution
set.seed(1101)
v = sqrt(rchisq(10000, 5)/5)
S = chol(R)
S = matrix(rnorm(10000 * 5), 10000) %*% S
ret = S/v
RT = as.matrix(t(apply(ret, 1, FUN = function(x) x*sigma+mu)))
# setup the functions
.VaR = function(x, alpha = 0.05)
{
VaR = quantile(x, probs = alpha, type = 1)
VaR
}
.CVaR = function(x, alpha = 0.05)
{
VaR = .VaR(x, alpha)
X = as.vector(x[, 1])
CVaR = VaR - 0.5 * mean(((VaR-X) + abs(VaR-X))) / alpha
CVaR
}
.fn1 = function(x,ret)
{
port=ret%*%x
obj=-.CVaR(-port)/.CVaR(port)
return(obj)
}
# abs(sum) of weights ==1
.eqn1 = function(x,ret)
{
sum(abs(x))
}
LB=rep(0,5)
UB=rep(1,5)
pars=rep(1/5,5)
ctrl = list(delta = 1e-10, tol = 1e-8, trace = 0)
cl = makePSOCKcluster(2)
# export the auxilliary functions which are used and cannot be seen by gosolnp
clusterExport(cl, c(".CVaR", ".VaR"))
ans = gosolnp(pars, fun = .fn1, eqfun = .eqn1, eqB = 1, LB = LB, UB = UB,
n.restarts = 2, n.sim=500, cluster = cl, ret = RT)
ans
# don't forget to stop the cluster!
stopCluster(cl)
## End(Not run)
``` |

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