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[mathematical modeling] summary of common basic models
2022-07-26 13:53:00 【Muxi Krystal】
Linear programming (Linear Programming)
- An important branch of operations research —— Mathematical programming . Linear programming is limited by a set of linear constraints , The problem of finding the maximum or minimum of a linear objective function .
- Concept : Feasible solution 、 Optimal solution 、 The feasible region .
- Matlab The order for solving linear programming in is as follows ,x Returns the value of the decision vector ;fval Returns the optimal value of the objective function ;f It's the value vector .
[x,fval] = linprog(f,A,b,Aeq,beq,lb,ub)
- Many problems that don't seem to be linear programming , It can also be solved by transforming the problem into linear programming .
For example, for the following mathematical programming problems :
min ∣ x 1 ∣ + ∣ x 2 ∣ + ⋯ + ∣ x n ∣ s.t. A x ⩽ b ∘ \begin{array}{c} \min \left|x_{1}\right|+\left|x_{2}\right|+\cdots+\left|x_{n}\right| \\ \text { s.t. } \boldsymbol{A} \boldsymbol{x} \leqslant \boldsymbol{b}_{\circ} \end{array} min∣x1∣+∣x2∣+⋯+∣xn∣ s.t. Ax⩽b∘
Just notice the facts :
For any x i x_i xi, There is u i , v i ≥ 0 u_i,v_i \ge 0 ui,vi≥0 Satisfy x i = u i − v i , ∣ x i ∣ = u i + v i x_{i}=u_{i}-v_{i},\left|x_{i}\right|=u_{i}+v_{i } xi=ui−vi,∣xi∣=ui+vi
So take u i = x i + ∣ x i ∣ 2 , v i = ∣ x i ∣ − x i 2 u_{i}=\frac{x_{i}+\left|x_{i}\right|}{2}, v_{i}=\frac{\left|x_{i}\right|-x_{i}}{2} ui=2xi+∣xi∣,vi=2∣xi∣−xi, remember u = [ u 1 , ⋯ , u n ] T , v = [ v 1 , ⋯ , v n ] T \boldsymbol{u}=\left[u_{1}, \cdots, u_{n}\right]^{\mathrm{T}}, \boldsymbol{v}=\left[v_{1}, \cdots, v_{n}\right]^{\mathrm{T}} u=[u1,⋯,un]T,v=[v1,⋯,vn]T,
So as to rewrite the model into :
min ∑ i = 1 n ( u i + v i ) , s.t. { [ A , − A ] [ u v ] ⩽ b , u , v ⩾ 0 . \begin{array}{ll} \min & \sum_{i=1}^{n}\left(u_{i}+v_{i}\right), \\ \text { s.t. } \quad & \left\{\begin{array}{l} {[\boldsymbol{A},-\boldsymbol{A}]\left[\begin{array}{l} \boldsymbol{u} \\ \boldsymbol{v} \end{array}\right] \leqslant \boldsymbol{b},} \\ \boldsymbol{u}, \boldsymbol{v} \geqslant 0 . \end{array}\right. \end{array} min s.t. ∑i=1n(ui+vi),⎩⎨⎧[A,−A][uv]⩽b,u,v⩾0.
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