本文整理汇总了Python中pyscipopt.Model.setMaximize方法的典型用法代码示例。如果您正苦于以下问题：Python Model.setMaximize方法的具体用法？Python Model.setMaximize怎么用？Python Model.setMaximize使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyscipopt.Model的用法示例。
在下文中一共展示了Model.setMaximize方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_model
# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import setMaximize [as 别名]
def test_model():
# create solver instance
s = Model()
# add some variables
x = s.addVar("x", vtype = 'C', obj = 1.0)
y = s.addVar("y", vtype = 'C', obj = 2.0)
assert x.getObj() == 1.0
assert y.getObj() == 2.0
s.setObjective(4.0 * y + 10.5, clear = False)
assert x.getObj() == 1.0
assert y.getObj() == 4.0
assert s.getObjoffset() == 10.5
# add some constraint
c = s.addCons(x + 2 * y >= 1.0)
assert c.isLinear()
s.chgLhs(c, 5.0)
s.chgRhs(c, 6.0)
assert s.getLhs(c) == 5.0
assert s.getRhs(c) == 6.0
# solve problem
s.optimize()
solution = s.getBestSol()
# print solution
assert (s.getVal(x) == s.getSolVal(solution, x))
assert (s.getVal(y) == s.getSolVal(solution, y))
assert round(s.getVal(x)) == 5.0
assert round(s.getVal(y)) == 0.0
assert s.getSlack(c, solution) == 0.0
assert s.getSlack(c, solution, 'lhs') == 0.0
assert s.getSlack(c, solution, 'rhs') == 1.0
assert s.getActivity(c, solution) == 5.0
s.writeProblem('model')
s.writeProblem('model.lp')
s.freeProb()
s = Model()
x = s.addVar("x", vtype = 'C', obj = 1.0)
y = s.addVar("y", vtype = 'C', obj = 2.0)
c = s.addCons(x + 2 * y <= 1.0)
s.setMaximize()
s.delCons(c)
s.optimize()
assert s.getStatus() == 'unbounded'示例2: test_knapsack
# 需要导入模块: from pyscipopt import Model [as 别名]
# 或者: from pyscipopt.Model import setMaximize [as 别名]
def test_knapsack():
# create solver instance
s = Model("Knapsack")
s.hideOutput()
# setting the objective sense to maximise
s.setMaximize()
# item weights
weights = [4, 2, 6, 3, 7, 5]
# item costs
costs = [7, 2, 5, 4, 3, 4]
assert len(weights) == len(costs)
# knapsack size
knapsackSize = 15
# adding the knapsack variables
knapsackVars = []
varNames = []
varBaseName = "Item"
for i in range(len(weights)):
varNames.append(varBaseName + "_" + str(i))
knapsackVars.append(s.addVar(varNames[i], vtype='I', obj=costs[i], ub=1.0))
# adding a linear constraint for the knapsack constraint
coeffs = {knapsackVars[i]: weights[i] for i in range(len(weights))}
s.addCons(coeffs, lhs=None, rhs=knapsackSize)
# solve problem
s.optimize()
s.printStatistics()
# retrieving the best solution
solution = s.getBestSol()
# print solution
varSolutions = []
for i in range(len(weights)):
solValue = round(s.getVal(knapsackVars[i], solution))
varSolutions.append(solValue)
if solValue > 0:
print (varNames[i], "Times Selected:", solValue)
print ("\tIncluded Weight:", weights[i]*solValue, "\tItem Cost:", costs[i]*solValue)
s.releaseVar(knapsackVars[i])
includedWeight = sum([weights[i]*varSolutions[i] for i in range(len(weights))])
assert includedWeight > 0 and includedWeight <= knapsackSize