EXERCISE 1.
NUMERICAL OPTIMISATION ASSIGNMENT 0: EXAMPLE
MARTA BETCKE KIKO RUL·LAN
- (a) Write a Matlab function that implements the Rosenbrock function f(x,y)=100(y−x2)2 +(1−x)2.
Be careful to implement a function that can be evaluated at many points simulta- neously.
Submit your implementation via Cody Coursework. - (b) Create a two dimensional grid using Matlab’s command meshgrid. Plot f using your implemented Matlab’s function on the grid. Check out the following func- tions: surf (use option ’EdgeColor’ = ’none’ when using many grid points), surfc, contour, contourf. Can you see the minimiser? Can you use some transformation to highlight the minimiser?
Submit your solution via Turnitin.
- (c) Calculate the gradient ∇f and the Hessian ∇2f.
Submit your solution via Turnitin.
- (d) Find the minimiser x∗ of the function f. Show that x∗ is unique and that ∇2f(x∗) is positive definite.
Submit your solution via Turnitin. - (e) Compute the gradient ∇f and the Hessian ∇2f numerically using finite differences. Check out the functions gradient, diff, and implement finite differences as a matrix multiplication.
Submit your solution via Turnitin.
Remark. The submission to Turnitin should not be longer than 2 pages. Avoid submitting more code than needed (if any) and focus on explaining your results.
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