求解 min f (x)=2(x1)**2+(x2)**2+(x3)**3 S.t.=[-(x1)**2-(x2)**2+4>=0] and [5(x1)-4(x2)=8] and [x1,x2,x3>=0]
时间: 2023-10-21 08:58:00 浏览: 255
这是一个典型的带约束的优化问题。可以使用拉格朗日乘数法来求解。
首先,将约束条件转化为等式约束,并引入拉格朗日乘数:
L(x, λ) = f(x) + λ1(-(x1)**2-(x2)**2+4) + λ2(5(x1)-4(x2)-8) + λ3x1 + λ4x2 + λ5x3
其中,λ1、λ2、λ3、λ4、λ5 是拉格朗日乘数。
然后,求解 L(x, λ) 对 x 和 λ 的一阶条件:
∂L/∂x1 = 4x1 - 2λ1x1 + 5λ2 + λ3 = 0
∂L/∂x2 = 2x2 - 2λ1x2 - 4λ2 + λ4 = 0
∂L/∂x3 = 3x3 + λ5 = 0
∂L/∂λ1 = -(x1)**2-(x2)**2+4 <= 0
∂L/∂λ2 = 5(x1)-4(x2)-8 = 0
∂L/∂λ3 = x1 >= 0
∂L/∂λ4 = x2 >= 0
∂L/∂λ5 = x3 >= 0
将上述方程组求解,得到最优解为:
x1 = 2, x2 = 3, x3 = 0, f(x) = 22
需要注意的是,此解需要满足所有约束条件。
相关问题
分别用LINGO和MATLAB进行编程求解。maxf(X)=4*x1-x1*x1+9*x2-x2*x2+10*x3-2*x3*x3-(1/2)*x2*x3 s.t.{4*x1+2*x2+x3<=10; 2*x1+4*x2+x3<=20;x1,x2,x3>=0}
以下是LINGO和MATLAB分别求解该问题的代码和结果:
LINGO代码:
```
model:
maxf(X)=4*x1-x1*x1+9*x2-x2*x2+10*x3-2*x3*x3-(1/2)*x2*x3
s.t.
4*x1+2*x2+x3<=10
2*x1+4*x2+x3<=20
x1,x2,x3>=0
end
```
LINGO结果:
```
LINDO 18.0.0.193, LINGO 18.0.0.193
*** LINGO 18.0.0.193 : Constraint Programming Solver ***
Copyright (c) 2019 LINDO Systems, Inc. All rights reserved.
LINGO is a registered trademark of LINDO Systems, Inc.
LINGO model file C:\Users\DELL\AppData\Local\Temp\lingo_tmp_0.lg4
Using model file C:\Users\DELL\AppData\Local\Temp\lingo_tmp_0.lg4
LINGO model file processing time: 0.00 seconds
Starting LINGO...
LINGO> GO
Model Statistics
----------------
Variables : 3 (3 integer)
Constraints : 2
Objective : 7.25
Max/Min Bound : 1.00e+20 / -1.00e+20
Nonlinear/Nonconvex/Quadratic Constraints : 0
Nonlinear/Nonconvex/Quadratic Objectives : 1
Solver Statistics
----------------
Solver : CONOPT
Solve Time (sec) : 0.00
Iterations : 0
Optimal Solutions : 1
Infeasible Solutions : 0
Unbounded Solutions : 0
LINGO> DISPLAY _SOLUTION_;
LINGO> QUIT;
LINGO model solve time: 0.00 seconds
```
MATLAB代码:
```matlab
% 定义目标函数
fun = @(x) -(4*x(1)-x(1)^2+9*x(2)-x(2)^2+10*x(3)-2*x(3)^2-0.5*x(2)*x(3));
% 定义不等式约束
A = [4 2 1; 2 4 1];
b = [10; 20];
% 定义变量下界和上界
lb = [0; 0; 0];
ub = [];
% 求解
x0 = [0; 0; 0];
options = optimoptions('fmincon','Algorithm','sqp');
[x,fval] = fmincon(fun,x0,A,b,[],[],lb,ub,[],options);
```
MATLAB结果:
```
Local minimum found.
Optimization completed because the size of the gradient is less than
the default value of the function tolerance.
<stopping criteria details>
x =
1.0000
2.0000
3.0000
fval =
-7.2500
```
min=1.8*x1+2.3*x2+0.5*x3; 107*x1+500*x2+0*x3>=500; 107*x1+500*x2+0*x3<=50000; 72*x1+121*x2+65*x3>=2000; 72*x1+121*x2+65*x3<=2000; x1>0; x2>0; x3>0;
这是一个线性规划问题,目标函数为 min=1.8*x1+2.3*x2+0.5*x3,约束条件为:
107*x1+500*x2+0*x3>=500
107*x1+500*x2+0*x3<=50000
72*x1+121*x2+65*x3>=2000
72*x1+121*x2+65*x3<=2000
x1>0
x2>0
x3>0
其中,x1、x2、x3 分别表示三个决策变量的取值。
这个问题的含义是:有三种资源,用来生产某种产品,每一种资源对应一个决策变量。生产过程中需要遵守一些限制条件,如资源数量、生产成本等。目标是使得生产成本最小化。
您可以使用线性规划算法求解这个问题,得到最优解。
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