Optimization and linear programming (StatQuest)
Intermediate
Optimization
by Best
Linear programming and the simplex algorithm: maximize an objective under constraints by walking the feasible region from vertex to vertex. Based on Josh Starmer's StatQuest lectures.
University approvals: 0
The cookie and donut factory, the feasible region and its vertices, why the optimum is always at a corner, and the main idea of the simplex algorithm: walk from vertex to vertex, uphill, until you cannot do better.
The vocabulary of a linear program, why the region must be convex, converting constraints to standard form, adding slack variables, building the tableau, and running pivots (entering column, ratio test, row reduction) until the top row has no negatives left.
- The vocabulary of a linear program Card
- Why the region has to be convex Card
- Standard form: making every constraint a less-than-or-equal Card
- Slack variables turn inequalities into equalities Card
- Building the simplex tableau Card
- One pivot: entering column, ratio test, and row reduction Card
- Iterating to the optimum, and a three-variable run Card