Matlab 中的 Gurobi 求解器
我想在Matlab中使用Gurobi求解器，但我不知道如何计算所需的矩阵（qrow和qcol）。
为了供您参考，我复制了文档中提供的示例。
0.5 x^2 - xy + y^2 - 2x - 6y
这是否
x + y <= 2
-x + 2y <= 2, 2x + y <= 3, x >= 0, y >= 0
c = [-2 -6]; % objective linear term
objtype = 1; % minimization
A = sparse([1 1; -1 2; 2 1]); % constraint coefficients
b = [2; 2; 3]; % constraint right-hand side
lb = []; % [ ] means 0 lower bound
ub = []; % [ ] means inf upper bound
contypes = '$<<
vtypes = [ ]; % [ ] means all variables are continuous
QP.qrow = int32([0 0 1]); % indices of x, x, y as in (0.5 x^2 - xy + y^2); use int64 if sizeof(int) is 8 for you system
QP.qcol = int32([0 1 1]); % indices of x, y, y as in (0.5 x^2 - xy + y^2); use int64 if sizeof(int) is 8 for you system
QP.qval = [0.5 -1 1]; % coefficients of (0.5 x^2 - xy + y^2)
意味着如果我有 4 个决策变量，那么我应该使用 0,1,2,3 作为我的决策变量 x_1, x_2, x_3, x_4 的索引？
谢谢
注意：我尝试使用 mathurl.com 但我不知道如何以正确的格式编写表明它将显示为乳胶文本。抱歉，没有注释。
I want to use Gurobi solver in Matlab, but I don't know how to calculate the required matrices (qrow and qcol).
For your reference I am copying the example provided in documentation.
0.5 x^2 - xy + y^2 - 2x - 6y
subject to
x + y <= 2
-x + 2y <= 2, 2x + y <= 3, x >= 0, y >= 0
c = [-2 -6]; % objective linear term
objtype = 1; % minimization
A = sparse([1 1; -1 2; 2 1]); % constraint coefficients
b = [2; 2; 3]; % constraint right-hand side
lb = []; % [ ] means 0 lower bound
ub = []; % [ ] means inf upper bound
contypes = 'lt;<
vtypes = [ ]; % [ ] means all variables are continuous
QP.qrow = int32([0 0 1]); % indices of x, x, y as in (0.5 x^2 - xy + y^2); use int64 if sizeof(int) is 8 for you system
QP.qcol = int32([0 1 1]); % indices of x, y, y as in (0.5 x^2 - xy + y^2); use int64 if sizeof(int) is 8 for you system
QP.qval = [0.5 -1 1]; % coefficients of (0.5 x^2 - xy + y^2)
Does it mean that if I have 4 decision variables than i should use 0,1,2,3 as indices for my decision variables x_1, x_2, x_3, x_4.?
Thanks
Note: I tried to use mathurl.com but I don't get how to write in proper format show that it will appear as latex text. Sorry for the notation.
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这似乎是您的参考。然而你的问题似乎与不同的例子有关。您可能需要展示这一点。
无论如何，根据 Gurobi 文档：
目标函数中的二次项应由 opts.QP.qrow、opts.QP.qcol 和 opts.QP.qval 指定，它们对应于函数 GRBaddqpterms 的输入参数 qrow、qcol 和 qval。它们都是一维数组。前两个参数 qrow 和 qcol 指定二阶项（例如 和 ）的行索引和列索引（从 0 开始）。第三个参数 qval 给出了它们的系数。
所以答案是肯定的，使用索引 [0 1 2 3] 作为决策变量 x0、x1、x2、x3。
This seems to be your reference. However your question seems to relate different example. You may need to show that one.
Anyway according Gurobi documentation:
The quadratic terms in the objective function should be specified by opts.QP.qrow, opts.QP.qcol, and opts.QP.qval, which correspond to the input arguments qrow, qcol, and qval of function GRBaddqpterms. They are all 1D arrays. The first two arguments, qrow and qcol, specify the row and column indices (starting from 0) of 2nd-order terms such as and . The third argument, qval, gives their coefficients.
So the answer is yes use indicies [0 1 2 3] for your decision variables x0, x1, x2, x3.