in this problem we study a natural method for

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: the optimal L2 approximation and (discretized) L1 optimal approximation for K = 10. You can find the L2 optimal approximation analytically, or by solving a least-squares problem associated with the discretized version of the problem. Since y is even, you can take the sine coefficients in your approximations to be zero. Show y and the two approximations on a single plot. In addition, plot a histogram of the residuals (i.e., the numbers f (ti ) − y (ti )) for the two approximations. Use the same horizontal axis range, so the two residual distributions can easily be compared. (Matlab command hist might be helpful here.) Make some brief comments about what you see. 5.4 Penalty function approximation. We consider the approximation problem minimize φ(Ax − b) where A ∈ Rm×n and b ∈ Rm , the variable is x ∈ Rn , and φ : Rm → R is a convex penalty function that measures the quality of the approximation Ax ≈ b. We will consider the following choices of penalty function: 40 (a) Euclidean norm. m φ(y ) = y 2 2 yk )1/2 . =( k=1 (b) ℓ1 -norm. m φ(y ) = y 1 = k=1 | yk | . (c) Sum of the largest m/2 absolute values. ⌊m/2⌋ |y |[k]...
View Full Document

Ask a homework question - tutors are online