Vectors, matrices, and convex problems — the toolkit behind portfolio weights and gradient descent.
Gradient descent minimizes a function by repeatedly stepping in the direction of steepest decrease. It is the workhorse that fits nearly every model an agent relies on — and portfolio weights too.