TOMLAB OPTIMIZATION ENVIRONMENT: llsAssign

llsAssign

Purpose

For setting up a linear least squares problem.

Syntax

   Prob = llsAssign(C, y, x_L, x_U, Name, x_0, ...
                    t, weightType, weightY, ...
                    A, b_L, b_U, ... 
                    x_min, x_max, f_opt, x_opt);

Description

llsAssign implements the TOMLAB Quick (TQ) format for the linear least squares problem.

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

Input Parameters

Call with at least five parameters

 C           Matrix m x n in objective ||Cx -y(t)||
 y           Vector m x 1 with observations in objective ||Cx -y(t)||
 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 

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

 t           m x 1 vector with time values (if empty assumed to be 1:m)
 weightType  Type of weighting
 weightY     Vector of weights

Linear Constraints

 A           Matrix A in 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. 

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

lcpAssign

lpAssign