Knapsack Optimization with Python

The purpose of the knapsack problem is to select which items to fit into the bag without exceeding a weight limit of what can be carried. We solve the problem with an integer programming solver (APOPT) by setting up each item as a binary variable (0 or 1).
📙 Knapsack Optimization: apmonitor.com/...
Objective: Maximize the value of items that can fit into a knapsack without exceeding a maximum weight constraint.
There are 4 items available to be placed in a knapsack: a towel, hammer, wrench, and screwdriver. The value and weight of the items are listed in the table below.
Towel Hammer Wrench Screwdriver
Item Value (vi) 11 8 3 6
Item Weight (wi) 3 5 7 4

[@sc0v0ne]

Very good classroom, thanks Professor

[@lucasbernardoni]

Helped me a lot! Thanks for sharing this amazing content :)

[@supriyamanna715]

proff, is this mixed integer solution taking linear time for selecting the items needed?
the greedy one takes n logn as we have to sort and then take the ones needed

[@apm]

I’m not sure there is a deterministic scale up factor proven for mixed integer linear programming. The solution speed often depends on tuning parameters, such as gap tolerance.

[@hassanlaqrabti4036]

Professor do you see that I’m in necessity to learn MATLAB as I used to use python

[@apm]

Python is a powerful tool, but you can also think of it as a Matlab toolbox. Here is a tutorial on calling Python from Matlab: apmonitor.com/che263/index.php/Main/MatlabCallsPython