Hi, thank you very much for your response!

I have been trying to solve an LP with the dual simplex method. I’d like to have a warm start that is not necessarily feasible. I’ll provide a simple code example that shows what I’m trying to do.

import Clp

import Gurobi

import Random

using JuMP

using Gurobi

model = Model(Gurobi.Optimizer)

@variable(model, x[1:2] >= 0)

@objective(model, Max, 2*x[1] + x[2])

@constraint(model, c1, x[1] <= 1)

@constraint(model, c3, x[2] <= 1)

set_optimizer_attribute(model, “Method”, 1) #use dual simplex

set_optimizer_attribute(model, “Presolve”, 0)

set_start_value(x[1], 0.75)

set_start_value(x[2], 0.75)

print(model)

optimize!(model)

The output indicates that the warm start is being ignored. I tried to work around this by adding a superfluous binary variable to the model. This actually worked, and seemed to provide warm starts given that they were feasible. However, when I tried to provide an infeasible initial solution (1.5, 1.5), an error message appears indicating that the warm start is being ignored:

“User MIP start did not produce a new incumbent solution

User MIP start violates constraint R0 by 0.500000000”

Also, regarding PStart - I will try setting the DStart values as well as the previous user suggested. However, it seems like the attribute is unrecognized. When I try “x[1].Start(0.5)” I get the error message “type VariableRef has no field Start”, and a similar message when trying to use PStart.

Thanks again for you help and advice!