Model("Example") # 创建变量 x = MODEL.addVars(6, lb=0, ub=1, name='x') # 更新变量环境 MODEL.update() # 创建目标函数 MODEL.setObjective(x.prod(c), gurobipy.GRB.MINIMIZE) # 创建约束条件 MODEL.addConstrs(x.prod(p[i]) >= r[i] for i in range(3)) # 执行线性规划模型 MODEL...
x = m.addVars(2,2)coeff= {(0,0):1, (0,1):2,(1,0):3,(1,1):4} expr = x.prod(coeff) # x[0,0] + 2*x[0,1] + 3*x[1,0] + 4*x[1,1] expr = x.prod(coeff, 1,"*") # 3*x[1,0] + 4*x[1,1] 网络流案例详解 案例源文件根目录\example\python\netflow.py ...
x= m.addVars(2,2)coeff= {(0,0):1, (0,1):2,(1,0):3,(1,1):4}expr= x.prod(coeff)# x[0,0] + 2*x[0,1] + 3*x[1,0] + 4*x[1,1]expr= x.prod(coeff,1,"*")# 3*x[1,0] + 4*x[1,1] 网络流案例详解 案例源文件根目录\example\python\netflow.py 该问题涉及到2...
model = Model("example") ``` 3. 定义变量和约束条件:在模型中定义变量和约束条件,例如线性规划问题中的系数矩阵A和常数向量b: ```python # 定义变量x1, x2, ..., xn model.addVars(n, "x1", "x2", ..., "xn") # 定义目标函数系数矩阵A model.addConstrs(n, "A", -1, 1) # 定义约束...
tupledict变量由model.addVars()或者multidict()函数创建,通过创建时使用的indices进行访问,同时具有select(),sum(),prod()的功能筛选元素并快速构建表达式。 model.addVars(*indices)方式创建的对象,本质上是多种集合的笛卡尔乘积的结果 x = m.addVars(2,3,vtype=GRB.BINARY)# 生成的元素是x(0,0) x(0,1...
For example, x = model.addVars([(1, 3), 7], ['a']) is not allowed, since the first argument for the first member would be (1, 3). Similarly, x = model.addVars([((1, 3),'a'), (7,'a')]) is also not allowed. ...
from gurobipy import Model, GRB # 创建模型 model = Model("example") # 定义变量 vars = model.addVars(3, vtype=GRB.INTEGER) # 设置变量的值为列表的元素 values = [10, 20, 30] for i in range(3): vars[i].setAttr('lb', values[i]) # 设置下界 vars[i].setAttr('ub', values[i]...
model = gp.Model("example") # 定义变量 x1, x2 = model.addVars(2, vtype=gp.GRB.CONTINUOUS) # 设置目标函数 model.setObjective(gp.LinExpr(c1, x1, x2), sense=gp.GRB.MAXIMIZE) # 添加限制条件 model.addConstrs(gp.LinConstr(A1, b1, sense=gp.GRB.LESS_EQUAL), gp.LinConstr(A2, b2, ...
Example #9 0 Show file def solve_model(model: gurobipy.Model): model.optimize() if model.getAttr("STATUS") != gurobipy.GRB.OPTIMAL: raise ValueError sol = model.getVars() out = "" value = 0 for v in sol: out += f"{v.VarName} {v.X}\n" value += 1 if v.X >= ...
feasopt The last modification example we consider is feasopt, which adds variables to existing constraints and also changes the optimization objective. Setting the objective to zero is straightforward. In C, set the Obj attribute to 0: for (j = 0; j < numvars; ++j) { error = GRBsetdbl...