pt_optim_entropy {ptable}

Maximum Entropy Approach

Description

Function to solve the non-linear optimization problem used within ptable().

Usage

pt_optim_entropy(
  optim = optim,
  mono = mono,
  v = v,
  variance = variance,
  lb = p_lb,
  ub = p_ub,
  ndigits
)

Arguments

optim

optimization parameter (1=default, 2-4=further test implementations)

mono

(logical) monotony parameter

v

(integer) vector with perturbation values (i.e. deviations to the original frequency)

variance

(numeric) variance parameter

lb

(integer) vector with lower bounds of the controls

ub

(integer) vector with upper bounds of the controls

ndigits

(integer) number of digits

Details

The main parameter is 'optim': In 'optim=1 to 3' the variance is stated as inequality constraint and in 'optim=4' the variance condition is stated as equality constraint.

Value

The return value contains a list with two elements:

"result"

optimal value of the controls

"iter"

number of iterations that were executed

Author(s)

Tobias Enderle, Sarah Giessing, Jonas Peter

See Also

Giessing, S. (2016), 'Computational Issues in the Design of Transition Probabilities and Disclosure Risk Estimation for Additive Noise'. In: Domingo-Ferrer, J. and Pejic-Bach, M. (Eds.), Privacy in Statistical Databases, pp. 237-251, Springer International Publishing, LNCS, vol. 9867.

Fraser, B. and Wooton, J.: A proposed method for confidentialising tabular output to protect against differencing. In: Monographs of Official Statistics. Work session on Statistical Data Confidentiality, Eurostat-Office for Official Publications of the European Communities, Luxembourg, 2006, pp. 299-302