# Ye Liu

• Total activity 18
• Last activity
• Member since
• Following 0 users
• Followed by 0 users
• Subscriptions 4

Recent activity by Ye Liu
• Thanks Mario for you reply!  Did you know by any chance the approximation of normal distribution? I have been looking it up and found all of them involve division. Since Gurobi cannot divide decisi...

• Hi Jaromił,  Thanks for responding! I tried the following but still didn't work        from gurobipy import *        def mycallback(model, where):            if where == GRB.Callback.MIPNODE:      ...

• Hi Jaromił, Thank you so much! Yes, you are right, I used the iis.ilp file to find out that I forgot to relax the bound to be negative infinite. One minor issue is that iis.ilp file cannot overwrit...

• Hi Jaromił, Thanks a lot for your solution! However, when I implemented the code, sometimes I have the following message: Encountered an attribute error. I don't know if I made a mistake somewhere:...

• Thank you so much, Jaromił! I tried all of your suggestions, and they are very helpful.  I still have one question:  Adjusting the MIPGap is very useful; I changed the gap to 0.2%. But still, there...

• Thank you so much,  Jaromił! I really appreciate your response! When I was trying to construct a minimal working example, I found out that I used a vector instead of a scaler for the coefficient fo...

• Jaromił, thank you so much for your answers, it worked! You really helped me move forward with my research project!!! One more question regarding your previous response on How do I model conditiona...

• Thank you so much, Jaromił!!! I redefine y_10 as a vector then it works! I thought the new value of y_10 will overwrite the previous value so that's why I only used one variable y_10 before.  The m...

• Thanks, Jaromił for answering this question! I tried what you suggested, the model is still not solvable when T=3 (it works when T=2). Here is the minimal reproducible example showing the issue: im...

• I do have another question regarding writing the constraint: y1_1[i] = min( s1_1[i], max(q-s2_1[i], 0) )   I wrote it into two constraints by adding two additional variables y1_10 and y_11, but it...