Exercises 02: Linear Algebra (P)Review

## Canonicalizing Things

1. Suppose we're given a vector u in rectangular coordinates u = (x,y). How do we convert it to polar coordinates (r,θ)?
2. Suppose we have two vectors u, v expressed in polar coordinates u = (r,θ) and v = (s,φ). How do we add them (assuming we also want the sum in polar coordinates)?
3. Suppose you have a vector x in the plane with components a1, a2, expressed in the basis u1, u2. How do you write this vector in the standard basis e1 = (1,0), e2 = (0,1)? Do not assume that u1 and u2 are orthonormal.
4. Now suppose x = (x1,x2) is instead expressed with respect to the standard basis e1 = (1,0), e2 = (0,1) i.e., x = x1 e1 + x2 e2. How do we re-write x in the orthonormal basis u1, u2? I.e., if we want to write it as x = a1 u1 + a2 u2, how do we get the coefficients a1, a2?
5. Suppose we have the same setup as in the previous question, but u1 and u2 are no longer orthonormal. How do we now re-write x in the basis u1, u2? I.e., how do we get the coefficients a1, a2? How does the answer to this question relate to the answer to the previous question?
6. Finally, suppose x is expressed in a non-orthonormal basis u1, u2, and we want to re-write it in another non-orthonormal basis v1, v2. What should we do?

## Don't Be Dense

A lot of the matrices we work with in computer graphics (and other fields, like vision and machine learning) have only a small number of nonzero entries. Consider for instance the identity matrix, which has 1's along the diagonal; all other entries are 0. Other common matrices (such as the graph Laplacian) will also be mostly 0's. For this reason there are two basic ways to store a matrix A:

• Dense—Store every single entry Aij, whether or not it's equal to zero.
• Sparse—Store only the entries where Aij does not equal zero (and implicitly assume all other entries are zero).

1. How might you encode dense matrices (i.e., what kind of data structure?).
2. How might you encode sparse matrices (i.e., what kind of data structure?).
3. What's the cost of storing an n x n identity matrix using the two encodings described above?