
Amogh Bhosekar
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Recent activity by Amogh Bhosekar-
Help with Implementation of Branch and Benders Cut in GurobiPy
Awaiting user inputHi I need to implement following features for solving a MIP Main goal here is to generate cuts from integral LP solutions to master problem using integer subproblems throughout the tree. My maste...
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Which solution is reported when I set the time limit for my Model.
AnsweredHello, I am running Gurobi to solve a Lagrangian Decomposition algorithm in which I divide the problem into subproblems 1 and 2 named as m1 and m2 in Gurobi. Both are minimization problems. Summati...
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Help with Implementation of Branch and Benders Cut in GurobiPy
Hello, I am trying to implement Branch and Benders Cut algorithm using GurobiPy. I do have the problem separated into master and sub problems. Since it is a mixed integer problem I need to implemen...
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Adding and referencing constraints
AnsweredHello, I am using Gurobipy to solve LP and I want to refer to a constraint to call the .pi attribute. For a single constraint such as constr = sub.addConstr(y1 - y2 == 0); I can refer to the pi a...
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I need help with LinExpr
AnsweredI am trying to code a simple objective function which works fine. I read that quicksum() isn't the best way to model large objective function. I would like to know how I can model my objective func...
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What are the efficient ways to improve running time
AnsweredHello, Please take a look at attached photo. What are the things that I can try to speed up the process. I have everything coded in gurobi-python. Are there things that I can try with Gurobi func...
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Returning the model in python gurobi interface from a function
AnsweredHello, I am trying to work on a model after it is created inside a function and returned Example: def createmodel(params): m = Model() x = m.addVar(vtype = GRB.BINARY) m.addConstr(con...
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How to return value of variables in python Gurobi interface
AnsweredHi, I have a MIP problem coded in python. Following is a code for illustration def problem(): m = Model() add variables : x = {} add constraints: ax <=b add objective: min cTx m.optimize() return ...