Я вижу, что люди с проблемой 10.0.1 выпускают версию downprading pip через python -m pip install... Вопрос по теме: optimization, python, pip, pycharm, gurobi.
I am using Gurobi (via C++) as part of my MSc thesis to solve Quadratic Knapsack Problem instances. So far, I was able to generate a model with binary decision variables, quadratic
I am trying to install Gurobi under Academic Licence on Ubuntu 14.04. I have read the following post about the UnsatisfiedLinkError: stackoverflow.com/questions/.... But that didnt
My OS is window 7, Pulp version is 1.6.1, gurobi version is 7.0.1. gurobipy can be successfully imported. pulp.solvers.GUROBI did pass the test, so I could use gurobi. However pulp
We are trying to run Gurobi through a Java web application with Tomcat server in a CentOS. System variables are defined: declare -x GRB_LICENSE_FILE='/home/suporte/gurobi.lic' decl
I'm looking for a way to save a presolved model in gurobi, so that I can save the time necessary for presolving the next time I'm running the model. I have tried to write the model
Can anybody help me with coding a 2-norm constraint? k=2 n=2 w = model.addMVar((k,n),lb = -1.0, ub = 1.0, vtype=gp.GRB.CONTINUOUS, name='w') for i in range (k): sumw = 0
I'm using the solver Gurobi with Java; I read all the Gurobi's Reference Manual, but I still have a few question it's possible to optimize a model without a objective function or
I'm using Gurobi in Python and for a given set S I'm adding the constraint as follows: for i in S: m.addConstr(quicksum(x[i,j] for j in (set(V) - set(S))) >= 2) I want to pri
I am trying to solve an LP problem represented using sparse matrices in Gurobi / python. max c′ x, subject to A x = b, L ≤ x ≤ U where A is a SciPy linked list
I am trying to run this example given in gurobi's example model. I am using python 3.5 with gurobi 7.0.2. When I run the code, I get the following error. Traceback (most recent cal
I'm trying to fix some constraints for the Graph coloring problem using networkx and gurobi. For each i ∈ V, we define the following set of intervals. Each interval [l,u] ∈ Ii
I have an optimization problem and I'm using Python and Gurobi to optimize it. In my problem formulation there is a constraint that has a nested sum. constraint I've recently start
I am using Gurobi 6.0 with Python 2.7. I am curious to know if Gurobi allows the objective function to have values coming from a dictionary with indices of the decision variables.
This is an attempt to answer the following question: https://matheducators.stackexchange.com/questions/11757/small-data-sets-with-integral-sample-standard-deviations So the intent
I encountered a quadratic constraint in my pyomo model. It worked more or less solving it with gurobi but it often gave me memory issues. So I linearized this quadratic constraint.
I'm using Julia's wonderful JuMP package to solve a linear program with Gurobi 6.0.4 as a solver. The objective function is a sum of decision variables, clearly defined as nonnegat
This is continuation of this thread. I am coding MILP using Gurobi in Python where the objective is to maximize the rewards while ensuring the distance constraint is not violated.
What are the advantages of using Gurobi with AMPL instead of using Gurobi direct API (java, C#, C++, etc.) for solving large MIP problems? Are there performance benefits when using
I'd like to set up an objective function in Gurobi to minimize x^2 + y^2. I've done my best to provide an example below: import gurobipy as gbPy model = gbPy.Model() A = [1, 2,
I am having trouble understanding why my code below is not producing the optimal result. I am attempting to create a step function but this clearly isn't working as the solution va
I download the Gurobi package for linear programming. I import the corresponding gurobi.jar package. Then run the example program. Then it appears the following errors: Exception
I am somewhat familiar with Gurobi, but transitioning to Gekko since the latter appears to have some advantages. I am running into one issue though, which I will illustrate using m
Any example for multi-objective optimization in Pyomo? I am trying to minimize 4 Objectives (Non Linear) and I would like to use pyomo and ipopt. Have also access to Gurobi. I wa
I am getting an error in my c++/gurobi file: Error code = 10004 Unable to retrieve attribute 'X' I read that this might have something to do with labels? But I don't see how there