SUMMARY
To find the gradient vector for a given point on a surface defined by the equation z = f(x,y), one must compute the gradient ∇f at that point, represented as ∇f = ⟨f_x, f_y⟩. The directional derivative D_u(f) is equal to zero when the gradient vector is orthogonal to the chosen unit vector u, defined as u = ⟨a, b⟩. This requires solving the equation ∇f · u = 0, which involves determining the appropriate direction for u based on the angle with the coordinate axes. The process may involve some complexity due to the three-dimensional nature of the problem.
PREREQUISITES
- Understanding of gradient vectors in multivariable calculus
- Familiarity with directional derivatives and their computation
- Knowledge of unit vectors and their properties
- Basic proficiency in solving equations involving vectors
NEXT STEPS
- Study the properties of gradient vectors in multivariable calculus
- Learn how to compute directional derivatives for functions of multiple variables
- Explore the geometric interpretation of gradients and directional derivatives
- Investigate applications of gradients in optimization problems
USEFUL FOR
Students and professionals in mathematics, physics, and engineering who are working with multivariable functions and need to understand gradient vectors and directional derivatives for optimization and analysis of surfaces.