Main

## Main.MatlabOptimization History

April 06, 2017, at 04:54 AM by 174.148.139.49 -

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<iframe width="560" height="315" src="https://www.youtube.com/embed/_Il7GQdL3Sk" frameborder="0" allowfullscreen></iframe>
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April 06, 2017, at 04:25 AM by 45.56.3.173 -
Changed lines 61-62 from:
lb = 1.0 * ones(5);
ub = 5.0 * ones(5);
to:
lb = 1.0 * ones(4);
ub = 5.0 * ones(4);
April 06, 2017, at 03:41 AM by 45.56.3.173 -
Changed line 37 from:
{$Aeq \,x = beq$}
to:
{$A_{eq} \,x = b_{eq}$}
April 06, 2017, at 03:38 AM by 45.56.3.173 -

The optimize toolbox in MATLAB has linear and nonlinear solvers. One of the most versatile is fmincon, a function minimizer with linear and nonlinear constraints. The fmincon function is:

= fmincon(FUN,X0,A,B,Aeq,Beq,LB,UB,NONLCON,OPTIONS)

Mathematically, this is written as:

{$\min fun(x)$}
{$\mathrm{s.t.} \quad A\,x \le b$}
{$Aeq \,x = beq$}
{$LB \le x \le UB$}
{$ceq(x)=0$}
{$c(x)<0$}
{$x_0 = \mathrm{initial\;guess}$}

Below is an example of solving the Hock Schittkowski problem #71, a minimal example that includes a nonlinear inequality and equality constraint. The nonlinear constraints are in a separate function named nlcon.m.

(:source lang=matlab:)
% create file nlcon.m for nonlinear constraints
function [c,ceq] = nlcon(x)
c = 25.0 - x(1)*x(2)*x(3)*x(4);
ceq = sum(x.^2) - 40;
(:sourceend:)

The objective function is defined as an inline function but it could also be in a separate script such as objective.m. The following script shows how to solve the problem and report the results.
April 06, 2017, at 03:28 AM by 45.56.3.173 -
Changed lines 7-10 from:
Mathematical optimization problems may include equality constraints (e.g. =), inequality constraints (e.g. <, <=, >, >=), objective functions, algebraic equations, differential equations, continuous variables, discrete or integer variables, etc. One example of an optimization problem from a benchmark test set is the Hock Schittkowski problem #71.

Attach:hs71
.gif
to:
Mathematical optimization problems may include equality constraints (e.g. =), inequality constraints (e.g. <, <=, >, >=), objective functions, algebraic equations, differential equations, continuous variables, discrete or integer variables, etc. One example of an optimization problem from a benchmark test set is the Hock Schittkowski problem #71.

{$\min x_1 x_4 \left(x_1 + x_2 + x_3\right) + x_3$}
{$\mathrm{s .t.} \quad x_1 x_2 x_3 x_4 \ge 25$}
{$x_1^2 + x_2^2 + x_3^2 + x_4^2 = 40$}
{$1\le x_1, x_2, x_3, x_4 \le 5$}
{$x_0 = (1,5,5,1)$}

!!!! Method #1: APM MATLAB

!!!! Method #2: MATLAB fmincon

(:source lang=matlab:)
objective = @(x) x(1)*x(4)*(x(1)+x(2)+x(3))+x(3);

% initial guess
x0 = [1,5,5,1];

% variable bounds
lb = 1.0 * ones(5);
ub = 5.0 * ones(5);

% show initial objective
disp(['Initial Objective: ' num2str(objective(x0))])

% linear constraints
A = [];
b = [];
Aeq = [];
beq = [];

% nonlinear constraints
nonlincon = @nlcon;

% optimize with fmincon
% = fmincon(FUN,X0,A,B,Aeq,Beq,LB,UB,NONLCON,OPTIONS)
x = fmincon(objective,x0,A,b,Aeq,beq,lb,ub,nonlincon);

% show final objective
disp(['Final Objective: ' num2str(objective(x))])

% print solution
disp('Solution')
disp(['x1 = ' num2str(x(1))])
disp(['x2 = ' num2str(x(2))])
disp(['x3 = ' num2str(x(3))])
disp(['x4 = ' num2str(x(4))])
(:sourceend:)
May 06, 2014, at 05:40 AM by 23.255.240.62 -
Changed line 17 from:
<iframe width="560" height="315" src="//www.youtube.com/embed/BZmSH8Y8_fc?list=PLLBUgWXdTBDi-E--rwBujaNkTejLNI6ap" frameborder="0" allowfullscreen></iframe>
to:
<iframe width="560" height="315" src="//www.youtube.com/embed/Q2zgz0ag0L0?list=PLLBUgWXdTBDi-E--rwBujaNkTejLNI6ap" frameborder="0" allowfullscreen></iframe>
May 06, 2014, at 05:09 AM by 23.255.240.62 -
May 06, 2014, at 04:27 AM by 23.255.240.62 -
Changed line 20 from:
This tutorial can also be completed with nonlinear programming optimizers that are available with the [[Main/ExcelSolver|Excel Solver]] and the [[Main/PythonOptimization|Python optimization packages in APMonitor]]. Click on the appropriate link for additional information and source code.
to:
This tutorial can also be completed with nonlinear programming optimizers that are available with the [[Main/ExcelSolver|Excel Solver]] and [[Main/PythonOptimization|Python Optimization]]. Click on the appropriate link for additional information and source code.
May 06, 2014, at 04:26 AM by 23.255.240.62 -
Changed lines 7-8 from:
Mathematical optimization problems may include equality constraints (e.g. =), inequality constraints (e.g. <, <=, >, >=), objective functions, algebraic equations, differential equations, continuous variables, discrete or integer variables, etc. One example of an optimization problem from a benchmark test set is the Hock Schittkowski problem #71. This problem has a nonlinear objective that the optimizer attempts to minimize. The variable values at the optimal solution are subject to (s.t.) both equality (=40) and inequality (>25) constraints. The product of the four variables must be greater than 25 while the sum of squares of the variables must also equal 40. In addition, all variables must be between 1 and 5 and the initial guess is x'_1_' = 1, x'_2_' = 5, x'_3_' = 5, and x'_4_' = 1.
to:
Mathematical optimization problems may include equality constraints (e.g. =), inequality constraints (e.g. <, <=, >, >=), objective functions, algebraic equations, differential equations, continuous variables, discrete or integer variables, etc. One example of an optimization problem from a benchmark test set is the Hock Schittkowski problem #71.
This problem has a nonlinear objective that the optimizer attempts to minimize. The variable values at the optimal solution are subject to (s.t.) both equality (=40) and inequality (>25) constraints. The product of the four variables must be greater than 25 while the sum of squares of the variables must also equal 40. In addition, all variables must be between 1 and 5 and the initial guess is x'_1_' = 1, x'_2_' = 5, x'_3_' = 5, and x'_4_' = 1.
-->Attach:hs71_MATLAB.png
May 06, 2014, at 04:23 AM by 23.255.240.62 -
Changed line 7 from:
Mathematical optimization problems may include equality constraints (e.g. =), inequality constraints (e.g. <, <=, >, >=), objective functions, algebraic equations, differential equations, continuous variables, discrete or integer variables, etc. One example of an optimization problem from a benchmark test set is the Hock Schittkowski problem #71. This problem has a nonlinear objective that the optimizer attempts to minimize. The variable values at the optimal solution are subject to (s.t.) both equality (=40) and inequality (>25) constraints. The product of the four variables must be greater than 25 while the sum of squares of the variables must also equal 40. In addition, all variables must be between 1 and 5 and the initial guess is x_1 = 1, x_2 = 5, x_3 = 5, and x_4 = 1.
to:
Mathematical optimization problems may include equality constraints (e.g. =), inequality constraints (e.g. <, <=, >, >=), objective functions, algebraic equations, differential equations, continuous variables, discrete or integer variables, etc. One example of an optimization problem from a benchmark test set is the Hock Schittkowski problem #71. This problem has a nonlinear objective that the optimizer attempts to minimize. The variable values at the optimal solution are subject to (s.t.) both equality (=40) and inequality (>25) constraints. The product of the four variables must be greater than 25 while the sum of squares of the variables must also equal 40. In addition, all variables must be between 1 and 5 and the initial guess is x'_1_' = 1, x'_2_' = 5, x'_3_' = 5, and x'_4_' = 1.
May 06, 2014, at 04:22 AM by 23.255.240.62 -
Changed line 7 from:
Mathematical optimization problems may include equality constraints (e.g. =), inequality constraints (e.g. <, <=, >, >=), objective functions, algebraic equations, differential equations, continuous variables, discrete or integer variables, etc. One example of an optimization problem from a benchmark test set is the Hock Schittkowski problem #71. This problem has a nonlinear objective and constraints with both equality and inequality constraints.
to:
Mathematical optimization problems may include equality constraints (e.g. =), inequality constraints (e.g. <, <=, >, >=), objective functions, algebraic equations, differential equations, continuous variables, discrete or integer variables, etc. One example of an optimization problem from a benchmark test set is the Hock Schittkowski problem #71. This problem has a nonlinear objective that the optimizer attempts to minimize. The variable values at the optimal solution are subject to (s.t.) both equality (=40) and inequality (>25) constraints. The product of the four variables must be greater than 25 while the sum of squares of the variables must also equal 40. In addition, all variables must be between 1 and 5 and the initial guess is x_1 = 1, x_2 = 5, x_3 = 5, and x_4 = 1.
May 06, 2014, at 04:17 AM by 23.255.240.62 -
(:title Optimization with MATLAB:)
(:keywords Optimization, Nonlinear Programming, MATLAB, spreadsheet, nonlinear, optimization, engineering optimization, university course:)
(:description Optimization with MATLAB - Problem-Solving Techniques for Chemical Engineers at Brigham Young University:)

Optimization deals with selecting the best option among a number of possible choices that are feasible or don't violate constraints. MATLAB can be used to optimize parameters in a model to best fit data, increase profitability of a potential engineering design, or meet some other type of objective that can be described mathematically with variables and equations.

Mathematical optimization problems may include equality constraints (e.g. =), inequality constraints (e.g. <, <=, >, >=), objective functions, algebraic equations, differential equations, continuous variables, discrete or integer variables, etc. One example of an optimization problem from a benchmark test set is the Hock Schittkowski problem #71. This problem has a nonlinear objective and constraints with both equality and inequality constraints.

Attach:hs71.gif

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<iframe width="560" height="315" src="//www.youtube.com/embed/BZmSH8Y8_fc?list=PLLBUgWXdTBDi-E--rwBujaNkTejLNI6ap" frameborder="0" allowfullscreen></iframe>
(:htmlend:)

This tutorial can also be completed with nonlinear programming optimizers that are available with the [[Main/ExcelSolver|Excel Solver]] and the [[Main/PythonOptimization|Python optimization packages in APMonitor]]. Click on the appropriate link for additional information and source code.

----

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