Optimize with Python

Engineering optimization platforms in Python are an important tool for engineers in the modern world. They allow engineers to quickly and easily optimize complex engineering problems and tasks, such as design optimization, resource allocation, and route planning. This notebook has examples for solving LP, QP, NLP, MILP, and MINLP problems in Python.
1️⃣ Linear Programming (LP)
2️⃣ Quadratic Programming (QP)
3️⃣ Nonlinear Programming (NLP)
4️⃣ Mixed Integer Linear Programming (MILP)
5️⃣ Mixed Integer Nonlinear Programming (MINLP)
🏫 Source Code with Jupyter Notebook: apmonitor.com/me575/index.php...
0:00 Optimize with Python
1:22 Linear Programming (LP)
9:53 Quadratic Programming (QP)
19:00 Nonlinear Programming (NLP)
24:16 Mixed Integer LP
29:00 Mixed Integer NLP
31:20 Box Folding MINLP

[@mohamedyusufmohamud8193]

Thank you ❤

[@kisvarosipari]

great tutorial

[@jakemohammd4279]

Thanks

[@kurniawanSA]

25:43 MILP

[@maximinmaster7511]

hello, your examples really speak to me, do you have other sources where I can find examples in the different models of scipy? THANKS

[@apm]

Sure, there are a few examples in the online courses for Process Dynamics and Control (apmonitor.com/pds), Design Optimization (apmonitor.com/me575) and Engineering Programming (apmonitor.com/che263). Here is one in particular: apmonitor.com/che263/index.php/Main/PythonOptimization

[@ahzanulkholish7419]

It's very interesting, thank you. Question, any idea how to visualize the last MINLP case ?

[@apm]

Sure, here is a contour plot of a related problem that shows the optimal steps towards the solution. You'd just need to modify this visualization to include integer points. apmonitor.com/me575/index.php/Main/BoxFolding

[@usefulknowledge6074]

I don't understand how the solution at min 23:16 is the correct one for the figure shown. The contour line with value 3 intersects the region at a point beyond x=1.75. Can you cross-check?

[@apm]

For minimization, there are two local solutions. When using the initial guess "x.value=2.25; y.value = 2", the solution is "x: 2.5 y: 2.0 obj: 0.25" (Global optimum). When using the initial guess "x.value=1.25; y.value = 3", the solution is "x: 1.0 y: 3.0 obj: 1.0". When switching to maximization with "m.Maximize(x*y**2-x**2-y**2)", there is one local / global solution at "x: 1.6666666667 y: 3.0 obj: 3.2222222222". Even though the line extends, it is not in the feasible region because y

[@user-ho3qc2sz5f]

Hi John, I want to maximize profit/revenue with binary variables, but result always coming as nan, can you tell why, I want to achieve max gross profit margin, trying to solve with gekko mixed integer non linear programming as you showed say whether products will be in mix or not by that binary variables will be 1 or 0, here is the example for 3 products, variables are x1, x2 and x3 total profit = 150*x1 + 120*x2 + 100*x3 total revenue = 200*x1 + 150*x2 + 250*x3 m = GEKKO() x1 = m.Var(integer=True, lb=0, ub=1) x2 = m.Var(integer=True, lb=0, ub=1) x3 = m.Var(integer=True, lb=0, ub=1) m.Maximize((150*x1 + 120*x2 + 100*x3)/(200*x1 + 150*x2 + 250*x3)) m.Equation(x1 + x2 + x3 = 2) m.options.SOLVER = 1 m.solve() it is giving me solution as all x is 0, objective function as nan I have tried only with the numerator i.e profit maximization, then it is working, but with the denominator it is not working thanks in advance

[@apm]

Great question - I think I have a solution. Could you ask the question on StackOverflow so that it is easier to find for others who have a similar question? stackoverflow.com/questions/tagged/gekko