a vector of doubles, represents the linear coefficients in the equality constraints
a vector of doubles, where n is number of variables, contains lower bounds of the variables.
a vector of doubles, contains lower bounds of the variables.
a vector of doubles, where n is number of variables, contains upper bounds of the variables.
a vector of doubles, contains upper bounds of the variables.
a vector of doubles, contains initial guess of variables.
a list containing the the parameters to be set.
a vector of doubles, the computed solution of the optimization problem.
a double, the function value at x.
a double, objective value returned as the scalar value norm(C*x-d)^2.
a vector of doubles, solution residuals returned as the vector C*x-d.
Integer identifying the reason the algorithm terminated.
Structure containing the Lagrange multipliers at the solution x (separated by constraint type).
Description
-Search the minimum of a constrained linear least square problem specified by : -find the minimum of f(x) such that
+Search the minimum of a constrained linear least square problem specified by :
We are calling IPOpt for solving the linear least square problem, IPOpt is a library written in C++. The code has been written by Andreas Wächter and Carl Laird.
+
lsqnonneg
+Solves nonnegative least-squares curve fitting problems.
Calling Sequence
+x = lsqnonneg(C,d) +x = lsqnonneg(C,d,param) +[xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg( ... )
Parameters
+- C : +
a matrix of doubles, represents the multiplier of the solution x in the expression C*x - d. C is M-by-N, where M is the number of equations, and N is the number of elements of x.
+ - d : +
a vector of doubles, represents the additive constant term in the expression C*x - d. d is M-by-1, where M is the number of equations.
+ - xopt : +
a vector of doubles, the computed solution of the optimization problem.
+ - resnorm : +
a double, objective value returned as the scalar value norm(C*x-d)^2.
+ - residual : +
a vector of doubles, solution residuals returned as the vector C*x-d.
+ - exitflag : +
Integer identifying the reason the algorithm terminated.
+ - output : +
Structure containing information about the optimization.
+ - lambda : +
Structure containing the Lagrange multipliers at the solution x (separated by constraint type).
Description
+Solves nonnegative least-squares curve fitting problems specified by :
+
We are calling IPOpt for solving the nonnegative least-squares curve fitting problems, IPOpt is a library written in C++. The code has been written by Andreas Wächter and Carl Laird.
+Examples
+A basic lsqnonneg problem +C = [ +0.0372 0.2869 +0.6861 0.7071 +0.6233 0.6245 +0.6344 0.6170]; +d = [ +0.8587 +0.1781 +0.0747 +0.8405]; +[xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg(C,d) |  |  |
Authors
+- Harpreet Singh
+ + + + diff --git a/help/en_US/scilab_en_US_help/qpipopt.html b/help/en_US/scilab_en_US_help/qpipopt.html index c0e1b71..7588c1d 100644 --- a/help/en_US/scilab_en_US_help/qpipopt.html +++ b/help/en_US/scilab_en_US_help/qpipopt.html @@ -12,7 +12,7 @@