Lecture 13 Flashcards
sensitivity of solutions linear system (perturbation to b)
||Δx|| / ||x|| <= cond(A) ||Δb||/||b||
condition number
output relerr / input relerr
= (||Δx||/||x||)/(||Δb||/||b||) <= = ||A^-1|| ||A|| = cond(A)
ill conditioned system
Amplifies error of the input (large cond(A))
well conditioned system
small cond(A)
sensitivity of solutions linear system (perturbation to A)
||Δx|| / ||x|| <= cond(A) ||ΔA||/||A||
norm condition number
||A^-1|| ||A|| = cond(A)
cons relative to a given norm, cond_2(A), cond_infty(A)…
What’s the best condition number
cond(A) = 1, indeed: cond(A) = ||A||||A^-1|| >= ||AA^-1|| = ||Id|| ||Id|| = max_{||x||=1} ||Id.x|| = max_{||x||=1} ||x|| = 1
Matrix with optimal conditioning
Orthogonal matrices have optimal conditioning (A-1 = A.T) and are very well-behaved in computation
Condition number of
100 0 0
0 13 0
0 0 0.5
cond(A) = ||A||||A^-1|| = 100.(1/0.5) = 200
Condition number properties (4)
cond(A) >= 1
cond(Id) = 1
cond(aA) = cond(A)
for any diagonal matrix D, cond(D)=max|di|/min|di|
Pivoting and singularity of the matrix relation
not related things, pivoting for numerical issues only (little c…)
condition number indicator
Measure of how close a matrix is to being singular (det(A) close to zero doesn’t mean anything!) large number is nearly singular, a cond close to 1 is far from being singular
residual error, error vector
||r|| = ||A ^x - b|| (no knowledge of x!) ||e|| = ||x - ^x||
Guaranteed small if LU with pivot?
regardless of the condition number, the relative residual is always small when LU with pivot (not the rel err)
||r|| / (||A|| ||^x||) <= ||ΔA||/||A|| <= c.em
Assuming IEEE double precision, how many digits of accuracy Ax=b when k(A)=1000?
10^3 10^-16 > 10^-13 digits of accuracy