Muhamad Fikri

Hi Jaromil, Thank you for your insight and suggestion! I managed to get the quadratic form to have the same optimal solution as the direct form by ignoring the Kronecker product. It is hilarious to...

Hi Maliheh, Thanks for your answer. I have some questions, especially regarding your comment. Let me highlight it first. "If you need an integral solution, you must formulate your problem as an i...

Hi Jaromil, Thank you for highlighting the result and reference from stack exchange! Best regards Muhamad

Hi Jaromil, When I used one of these three values     # cluster_size = np.array([62., 97.50, 95.50, 59.])     # cluster_size = np.array([117.5, 87.50, 119.])     cluster_size = np.array([187., 165....

Hi Jaromil, Sure, here is the model that I develop import numpy as npimport gurobipy as gpfrom gurobipy import GRB# Define QP formulationdef qp_calculation(values, clstr_size):    sensing_range = n...

Hi Jaromil, Thank you for the explanation! Yes, I want to know the possibility of convex quadratic to get integer optimal solutions. In my case, the constraints are linear and hold a property of to...

Hi,Thank you for the explanation. What I mean is I just want to know, if, in the initial stage, I have 12 continuous and 36 integers (36 binary). Then, the second one the continuous variable still ...

Hi, Thank you for your response. So, in my case, this information means that at the initial stage, I have 12 continuous and 36 integers (36 binary). Then, it is reduced to 0 continuous and 42 integ...

Hi Jaromil, Thank you for the insight. So far I understand the answer from me. It helps me a lot in understanding my learning      

Hi Jaromil Thank you for pointing out the mistake. I mistakenly wrote the constraints in the formulation. The one in the code was the correct one. Regarding the model in the .lp file, is the conten...