Ryuta Tamura

Hi Noah, This article might be useful. This means that adding the constraint for the case of the binary variable is 0. For example: model.addGenConstrIndicator(CL_time_bin[i, (t_start, t_end)], Tru...

This model will be Infeasible:  m = gp.Model()x = m.addVar(lb=10, ub=10, name="x")y = m.addVar(lb=0,ub=0, name="y")z = m.addVar(name="z")m.addGenConstrNL(z,x/y)m.optimize() As this description stat...

Hi, Please see this description. Gurobi implicitly tries to avoid division by zero. However, to avoid unexpected behavior, it is recommended to explicitly constrain the denominator to be non-zero s...

Hi Audrius, As the error message states, the objective function must be linear or quadratic. This can be resolved using auxiliary variable and addGenConstrNL method: obj = m.addVar(name="obj")m.add...

Hi Minseo, You can specify how to handle general function constraints by funcnonlinear parameter. When this parameter is 0, Gurobi uses static piecewise-linear approximation, and when it is 1, Guro...

Hi Mina, model.addGenConstrPow is used to create constraints \(x = n^a \) (x, n: variable, a: constant). You can use model.addGenConstrExpA to create constraints \(x = a^n \): model.addGenConstrExp...

Hi,Your model looks large, so your program may have crashed due to memory problems. Optimize a model with 126144161 rows, 132451320 columns and 129520768 nonzeros Checking memory usage may reveal t...

Hi, According to the part of your first post where you print input_vars: [<gurobi.LinExpr: 0.0 <gurobi.Var *Awaiting Model Update*>>, <gurobi.LinExpr: 0.0 <gurobi.Var *Awaiting Model Update*>>, <gu...

Hi, If I understand correctly, you are interested in enumerating "all" feasible solutions. In such use case, SolutionPool feature and PoolSearchMode parameter might help. By setting PoolSearchMode ...

Hi, The second argument of addGenConstrMax must be a list of "Var" object. It seems that your input_vars is a list of LinExpr object. In this case you can use auxiliary variables (e.g. vector a), a...