a double, objective value returned as the scalar value norm(C*x-d)^2.
a vector of double, solution residuals returned as the vector C*x-d.
a vector of double, solution residuals returned as the vector d-C*x.
Integer identifying the reason the algorithm terminated. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro.
A flag showing returned exit flag from Ipopt. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro.
Structure containing information about the optimization. This version only contains number of iterations.
Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper bound multiplier and linear equality, inequality constraints.
Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper bound multiplier and linear equality, inequality constraint multiplier.
Description
Search the minimum of a constrained linear least square problem specified by :
@@ -86,47 +86,41 @@Examples
//A simple linear least square example -C = [0.9501 0.7620 0.6153 0.4057 -0.2311 0.4564 0.7919 0.9354 -0.6068 0.0185 0.9218 0.9169 -0.4859 0.8214 0.7382 0.4102 -0.8912 0.4447 0.1762 0.8936]; -d = [0.0578 -0.3528 -0.8131 -0.0098 -0.1388]; -A = [0.2027 0.2721 0.7467 0.4659 -0.1987 0.1988 0.4450 0.4186 -0.6037 0.0152 0.9318 0.8462]; -b = [0.5251 -0.2026 -0.6721]; +C = [ 2 0; +-1 1; +0 2] +d = [1 +0 +-1]; +A = [10 -2; +-2 10]; +b = [4 +-4]; [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b) // Press ENTER to continue |  |  |
Examples
//A basic example for equality, inequality constraints and variable bounds -C = [0.9501 0.7620 0.6153 0.4057 -0.2311 0.4564 0.7919 0.9354 -0.6068 0.0185 0.9218 0.9169 -0.4859 0.8214 0.7382 0.4102 -0.8912 0.4447 0.1762 0.8936]; -d = [0.0578 -0.3528 -0.8131 -0.0098 -0.1388]; -A =[0.2027 0.2721 0.7467 0.4659 -0.1987 0.1988 0.4450 0.4186 -0.6037 0.0152 0.9318 0.8462]; -b =[0.5251 -0.2026 -0.6721]; -Aeq = [3 5 7 9]; -beq = 4; -lb = -0.1*ones(4,1); -ub = 2*ones(4,1); +C = [1 1 1; +1 1 0; +0 1 1; +1 0 0; +0 0 1] +d = [89; +67; +53; +35; +20;] +A = [3 2 1; +2 3 4; +1 2 3]; +b = [191 +209 +162]; +Aeq = [1 2 1]; +beq = 10; +lb = repmat(0.1,3,1); +ub = repmat(4,3,1); [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b,Aeq,beq,lb,ub) | | |
Authors
diff --git a/help/en_US/scilab_en_US_help/lsqnonneg.html b/help/en_US/scilab_en_US_help/lsqnonneg.html index e9a9110..7211c40 100644 --- a/help/en_US/scilab_en_US_help/lsqnonneg.html +++ b/help/en_US/scilab_en_US_help/lsqnonneg.html @@ -51,13 +51,13 @@a double, objective value returned as the scalar value norm(C*x-d)^2.
a vector of double, solution residuals returned as the vector C*x-d.
a vector of double, solution residuals returned as the vector d-C*x.
Integer identifying the reason the algorithm terminated. It could be 0, 1 or 2 i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded.
A flag showing returned exit flag from Ipopt. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro.
Structure containing information about the optimization. This version only contains number of iterations.
Structure containing the Lagrange multipliers at the solution x. It contains lower and upper bound multiplier.
Structure containing the Lagrange multipliers at the solution xopt. It contains lower, upper bound multiplier and linear equality, inequality constraint multiplier.
Description
Solves nonnegative least-squares curve fitting problems specified by :
@@ -67,16 +67,16 @@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]; +C = [1 1 1; +1 1 0; +0 1 1; +1 0 0; +0 0 1] +d = [89; +67; +53; +35; +20;] [xopt,resnorm,residual,exitflag,output,lambda] = lsqnonneg(C,d) | | |
Authors
diff --git a/help/en_US/scilab_en_US_help/qpipopt.html b/help/en_US/scilab_en_US_help/qpipopt.html index 349bbc4..31f389f 100644 --- a/help/en_US/scilab_en_US_help/qpipopt.html +++ b/help/en_US/scilab_en_US_help/qpipopt.html @@ -70,19 +70,40 @@a double, the function value at x.
Integer identifying the reason the algorithm terminated. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the qpipopt macro.
A flag showing returned exit flag from Ipopt. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro.
Structure containing information about the optimization. This version only contains number of iterations
Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints.
Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper bound multiplier and linear equality, inequality constraint multiplier.
Description
-Search the minimum of a constrained linear quadratic optimization problem specified by : -find the minimum of f(x) such that
+Search the minimum of a constrained linear quadratic optimization problem specified by :
The routine calls Ipopt for solving the quadratic problem, Ipopt is a library written in C++.
Examples
+//Ref : example 14 : +//https://www.me.utexas.edu/~jensen/ORMM/supplements/methods/nlpmethod/S2_quadratic.pdf +// min. -8*x1*x1 -16*x2*x2 + x1 + 4*x2 +// such that +// x1 + x2 <= 5, +// x1 <= 3, +// x1 >= 0, +// x2 >= 0 +H = [2 0 +0 8]; +f = [-8; -16]; +A = [1 1;1 0]; +conUB = [5;3]; +conLB = [-%inf; -%inf]; +lb = [0; 0]; +ub = [%inf; %inf]; +nbVar = 2; +nbCon = 2; +[xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB) +//Press ENTER to continue | | |
Examples
Examples
-//Find the value of x that minimize following function -// f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2 -// Subject to: -// x1 + x2 ≤ 2 -// –x1 + 2x2 ≤ 2 -// 2x1 + x2 ≤ 3 -// 0 ≤ x1, 0 ≤ x2. -H = [1 -1; -1 2]; -f = [-2; -6]; -A = [1 1; -1 2; 2 1]; -conUB = [2; 2; 3]; -conLB = [-%inf; -%inf; -%inf]; -lb = [0; 0]; -ub = [%inf; %inf]; -nbVar = 2; -nbCon = 3; -[xopt,fopt,exitflag,output,lambda] = qpipopt(nbVar,nbCon,H,f,lb,ub,A,conLB,conUB) | | |
Authors
- Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
a vector of double, the computed solution of the optimization problem.
a double, the function value at x.
a vector of double, solution residuals returned as the vector d-C*x.
Integer identifying the reason the algorithm terminated.It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the qpipoptmat macro.
A flag showing returned exit flag from Ipopt. It could be 0, 1 or 2 etc. i.e. Optimal, Maximum Number of Iterations Exceeded, CPU time exceeded. Other flags one can see in the lsqlin macro.
Structure containing information about the optimization. This version only contains number of iterations.
Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints.
Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper bound multiplier and linear equality, inequality constraint multiplier.
Description
-Search the minimum of a constrained linear quadratic optimization problem specified by : -find the minimum of f(x) such that
+Search the minimum of a constrained linear quadratic optimization problem specified by :
The routine calls Ipopt for solving the quadratic problem, Ipopt is a library written in C++.
Examples
-//Find the value of x that minimize following function -// f(x) = 0.5*x1^2 + x2^2 - x1*x2 - 2*x1 - 6*x2 -// Subject to: -// x1 + x2 ≤ 2 -// –x1 + 2x2 ≤ 2 -// 2x1 + x2 ≤ 3 -// 0 ≤ x1, 0 ≤ x2. -H = [1 -1; -1 2]; -f = [-2; -6]; -A = [1 1; -1 2; 2 1]; -b = [2; 2; 3]; + | | |
Authors
- Keyur Joshi, Saikiran, Iswarya, Harpreet Singh
a double, the function value at x.
status flag from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.
status flag returned from symphony. 227 is optimal, 228 is Time limit exceeded, 230 is iteration limit exceeded.
The output data structure contains detailed information about the optimization process. This version only contains number of iterations
Description
-Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by : -find the minimum or maximum of f(x) such that
+Search the minimum or maximum of a constrained mixed integer linear programming optimization problem specified by :
The routine calls SYMPHONY written in C by gateway files for the actual computation.
Examples
-//A basic case : + |
