Introduction to Optimization
\[ \begin{array}{lrrrrrrrrr} \mbox{maximize: } & c_0 & + & c_1 x_1 & + & \dots & + & c_n x_n & \\ \mbox{subject to: } & & & a_{11} x_1 & + & \dots & + & a_{1n} x_n & \leq & b_1 \\ & & & a_{21} x_1 & + & \dots & + & a_{2n} x_n & \leq & b_2 \\ & & & & & \vdots & \\ & & & a_{m1} x_1 & + & \dots & + & a_{mn} x_n & \leq & b_m \\ & & & x_1, & x_2, & \dots &, & x_n & \geq & 0 \end{array} \]
In Winter and Spring 2022 I taught a quarter-long course at Northwestern on Introduction to Optimization, covering mainly linear programming and duality theory.
I restructured the course around applications and modeling, wrote the course notes in RMarkdown, and built Excel worksheet assignments for the modeling scenarios.
The notes are accessible to students who have done linear algebra and some basic multivariable calculus (gradients).