Shapes of Quadratic Functions
In two dimensions, the graph of a quadratic form takes on beautifully distinct shapes depending on the signs of and . When both are positive, the graph is a bowl opening upward (elliptic paraboloid) — it has a unique global minimum at the origin. When both are negative, the bowl flips to open downward (unique global maximum). When and , the graph is a saddle — upward in one direction, downward in another, with no global minimum or maximum.
What if one eigenvalue is zero? When and , the surface is a half-pipe or parabolic cylinder — it curves up in the -direction but is flat in the -direction. Points along the -axis are all global minima (not a unique minimum). This geometric classification based on eigenvalue signs is exactly what definiteness formalizes (Section 8.16).
Formal View
For general symmetric , the shapes are the same but the "axes" are the eigenvectors of rather than the coordinate axes.
Interactive Visualization
Quadratic Surface Explorer
Why This Matters
The shape of a quadratic tells you everything about whether an optimization problem has a solution, and how robust that solution is.
- In machine learning, a "convex" loss function (bowl-shaped) guarantees that gradient descent converges to the unique global minimum.
- In game theory, saddle points correspond to Nash equilibria in zero-sum games.
- In structural mechanics, a half-pipe corresponds to a neutral mode — the structure can deform freely in one direction without any restoring force.
Quiz
The quadratic form has what shape?
If all eigenvalues of are positive, the quadratic has a unique global minimum at the origin.
Common Mistakes
- Thinking the shape depends on the entries of directly — it depends on the eigenvalues of (their signs determine the shape).
- Confusing a saddle point with a local minimum — at a saddle, the function decreases in some directions and increases in others.