Multivariable calculus
Gradient of x^3 + y^3 - 3xy: Steps and Critical Points
For f(x, y) = x^3 + y^3 - 3xy the gradient is (3x^2 - 3y, 3y^2 - 3x). At (2, 1) it equals (9, -3). Setting both components to zero gives exactly two critical points, the origin and (1, 1). The origin is a saddle and (1, 1) is a local minimum with value -1.
Three terms, and each partial keeps two of them
Hold fixed. The term is a pure constant and drops out, while has sitting in front of a single .
Hold fixed instead. Now is the constant that vanishes, and leaves behind.
At this gives . Notice the pleasing symmetry: swapping and in leaves it unchanged, and it swaps the two gradient components.
The mistake: letting the cube die in the wrong partial
The two frequent wrong answers are and . In each case the term has been used once and then forgotten, as though a term can only be spent on one component.
Each partial derivative is a fresh calculation on the entire function. The term contains both variables, so it appears in both components, contributing to one and to the other.
- vanishes under because it has no ; does not vanish because it does.
- The symmetry of is a free check. If your two components are not mirror images under swapping and , one of them is wrong.
- Slice check at : has derivative , which matches at .
Solving for the critical points
Set both components to zero. Dividing each by gives and , a pair of parabolas that cross where the gradient dies.
Substitute the first into the second: , so and . Over the reals that leaves and , giving the points and .
Classify them with the discriminant , where , and .
So the origin is a saddle, and has with , making it a local minimum with . It is not a global minimum: along the line , runs off to negative infinity.
Frequently asked questions
Why does x^3 + y^3 - 3xy have no global maximum or minimum?
The cubes dominate far from the origin. Along the function is , which goes to as grows and to as falls. A function unbounded in both directions can only have local extremes.
What is the direction of steepest descent at (2, 1)?
The negative gradient, , or as a unit vector . The function falls at rate in that direction.