TOMLAB OPTIMIZATION ENVIRONMENT: simAssign

simAssign

Purpose

For setting up constrained nonlinear programming problems, where both the function and the constraints need to be computed at the same time. This is often the case for simulation problems.

Syntax

   Prob = simAssign(fc, gdc, Hd2c, HessPattern, x_L, x_U, Name, x_0, ...
                    fLowBnd, A, b_L, b_U, ConsPattern, c_L, c_U, ... 
                    x_min, x_max, f_opt, x_opt);

Description

simAssign implements the TOMLAB Quick (TQ) format for (unconstrained and) constrained nonlinear programming problems, where both the function and the constraints need to be computed at the same time. This is often the case for simulation problems

simAssign is setting the variables normally needed for an optimization in the TOMLAB structure Prob.

Input Parameters

 fc          Name of the function that computes the function value f(x) 
             and the mN-vector of constraints c(x)
 gdc         Name of the function that computes the n x 1 gradient vector
             and the mN by n matrix dc with the constraint gradients
 Hd2c        Name of the function that computes the n x n Hessian matrix and
             the second part of the Lagrangian function (normally not used)
 HessPattern n x n zero-one sparse or dense matrix, where 0 values indicate 
             zeros in the Hessian and ones indicate values that might 
             be non-zero. If empty indicates estimation of all elements 
             HessPattern is used when estimating the Hessian numerically.
 x_L         Lower bounds on parameters x. If [] set as a nx1 -Inf vector.
 x_U         Upper bounds on parameters x. If [] set as a nx1  Inf vector.
 Name        The name of the problem (string)
 x_0         Starting values, default nx1 zero vector 

 Note:       The number n of the unknown variables x are taken as
             max(length(x_L),length(x_U),length(x_0))
             You must specifiy at least one of these with correct length,
             then the others are given default values

             The following parameters are optional, and problem type dependent
             Set empty to get default value

 fLowBnd     A lower bound on the function value at optimum. Default 0
             A good estimate is not critical. Use [] if not known at all.

Linear Constraints

 A           mA x n matrix A, linear constraints b_L<=A*x<=b_U. Dense or sparse
 b_L         Lower bound vector in linear constraints b_L <= A*x <= b_U. 
 b_U         Upper bound vector in linear constraints b_L <= A*x <= b_U. 

Nonlinear Constraints

 c_L         Lower bound vector in nonlinear constraints b_L <= c(x) <= b_U. 
 c_U         Upper bound vector in nonlinear constraints b_L <= c(x) <= b_U. 
 ConsPattern mN x n zero-one sparse or dense matrix, where 0 values indicate 
             zeros in the constraint Jacobian and ones indicate values that 
             might be non-zero. Used when estimating the Jacobian numerically.

Additional Parameters

 x_min   Lower bounds on each x-variable, used for plotting
 x_max   Upper bounds on each x-variable, used for plotting
 f_opt   Optimal function value(s), if known (Stationary points)
 x_opt   The x-values corresponding to the given f_opt, if known.
         If only one f_opt, give x_opt as a 1 by n vector
         If several f_opt values, give x_opt as a length(f_opt) by n matrix
         If adding one extra column n+1 in x_opt, 0 is min, 1 saddle, 2 is max.
         x_opt and f_opt is used in printouts and plots.

Set the variable as empty if this variable is not needed for the particular kind of problem you are solving.

sdpAssign

probAssign