Comparison of functions. Comparison of functions. Proof for reflexivity. Proof for transitivity. Reflexivity:

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1 Comparison of functions Comparison of functions Reading: Cormen et al, Section 3. (for slide 7 and later slides) Transitivity f (n) = (g(n)) and g(n) = (h(n)) imply f (n) = (h(n)) Reflexivity: f (n) = (f (n)) f (n) =O(f (n)) f (n) = (f (n)) f (n) =O(g(n)) and g(n) =O(h(n)) imply f (n) =O(h(n)) Reflexivity does not hold for o() and!( ). E.g., ln n 6= o(ln n). f (n) = (g(n)) and g(n) = (h(n)) imply f (n) = (h(n)) Symmetry: f (n) =o(g(n)) and g(n) =o(h(n)) imply f (n) =o(h(n)) f (n) = (g(n)) if and only if g(n) = (f (n)) f (n) =!(g(n)) and g(n) =!(h(n)) imply f (n) =!(h(n)) Examples: n = O(ln n) andlnn = O(n3 )imply n = O(n3 ). 3n 3 + n = (3n 3 )and3n 3 = (n 3 )imply3n 3 + n = (n 3 ). Symmetry does not hold for O(), (), o() and!(). For example, ln ln n = O(ln n), but ln n 6= O(ln ln n). Transpose symmetry: f (n) =O(g(n)) if and only if g(n) = (f (n)). f (n) =o(g(n)) if and only if g(n) =!(f (n)). 3 COMP3600/6466 Algorithms 08 Lecture 3 COMP3600/6466 Algorithms 08 Lecture 3 3 Proof for transitivity f (n) =O(g(n)) and g(n) =O(h(n)) imply f (n) =O(h(n)) f (n) =O(g(n)) means: There are constants c > 0andn > 0 such that 0 apple f (n) apple c g(n), for all n n. g(n) =O(h(n)) means: There are constants c > 0andn > 0 such that 0 apple g(n) apple c h(n), for all n n. Thus 0 apple f (n) apple c g(n) apple c (c h(n)), for all n max{n, n }. Since c = c c and max{n, n } are positive constants, it follows that f (n) = O(h(n)), by the definition of the O-notation. f (n) = (f (n)) Proof for reflexivity f (n) = (f (n)) means: There are constants c > 0andn 0 > 0 such that 0 apple cf (n) apple f (n), for n n 0. This is true, because f (n) apple f (n), for all positive integers, and f (n) is assumed to be asymptotically nonnegative. More precisely, c = and some large enough n 0 satisfy the definition, so f (n) = (f (n)) follows. COMP3600/6466 Algorithms 08 Lecture 3 COMP3600/6466 Algorithms 08 Lecture 3 4 4

2 Proof for symmetry Standard notations and common functions f (n) = (g(n)) if and only if g(n) = (f (n)) We show f (n) = (g(n)) implies g(n) = (f (n)). The reverse direction can be proven similarly. f (n) = (g(n)) means: There are constants c > 0, c > 0, and n 0 > 0 such that 0 apple c g(n) apple f (n) apple c g(n), for all n n 0. Since c g(n) apple f (n), we have g(n) apple c f (n). Similarly, since f (n) apple c g(n), we have g(n) c f (n), Monotonicity: A function f (n) ismonotonically increasing if m < n implies f (m) apple f (n) anditisstrictly increasing if m < n implies f (m) < f (n). Similarly, f (n) ismonotonically decreasing if m < n implies f (m) f (n) andstrictly decreasing if m < n implies f (m) > f (n). Floor and ceiling: For any real x, thefloor of x, denoted by bxc, is the greatest integer less than or equal to x. Theceiling of x, denoted by dxe, is the least integer greater than or equal to x. We have x < bxc applex appledxe < x +. E.g., b4.9c =4andd4.0e = 5. We thus have 0 apple c f (n) apple g(n) apple c f (n), i.e., there are constants c 0 = c > 0, c 00 = c > 0, and n 0 > 0 such that 0 apple c 0 f (n) apple g(n) apple c 00 f (n), for all n n 0. Thus g(n) = (f (n)), by the definition of the -notation. 5 Polynomials: Given a nonnegative integer d, apolynomial in n is a function of the form p(n) = P d i=0 a in i,wherea i is a constant for each i, calledacoe cient of the polynomial. The degree of a polynomial is the highest power that occurs. E.g., p(n) =3n 7 4n 5 +.n 00 is a polynomial of degree 7. 7 COMP3600/6466 Algorithms 08 Lecture 3 5 COMP3600/6466 Algorithms 08 Lecture 3 7 Proof for transpose symmetry f (n) =o(g(n)) if and only if g(n) =!(f (n)) Proof Suppose that f (n) = o(g(n)). Hence, for any positive constant c, there exists a positive constant n 0 such that 0 apple f (n) < cg(n), for all n n 0. Now let d be any positive constant. Then there exists a positive constant n 0 such that 0 apple f (n) < (/d)g(n), for all n n 0.That is, 0 apple df(n) < g(n), for all n n 0.Henceg(n) =!(f (n)). The converse is similar. So we can, in fact, define o() using!(), and conversely. COMP3600/6466 Algorithms 08 Lecture Standard notations and common functions Exponentials: For all a > 0, (a m ) n = a mn, a m a n = a m+n, lim n! ( + x n )n = e x, e ln x =ln(e x )=x. Factorials: Recall the definition of n factorial: n! = 3 (n ) n. Stirling s approximation: n! = p n n n + e n n! =o(n n ) n! =!( n ) lg(n!) = (n lg n) COMP3600/6466 Algorithms 08 Lecture 3 8 8

3 Standard notations and common functions 3 Logarithms: For all a > 0, b > 0, and c > 0, and log bases 6=, a = b log b a log b a = log c a log c b (Base of logarithm not important asymptotically) lg n log n ln n log e n lg k n =(lgn) k lg lg n =lg(lgn) A useful result from calculus (Apostol, Calculus, Volume, second edition, page 30: Iterated logarithm The iterated logarithm is a very slow growing function. lg =. lg 4 =. lg 6 = 3. lg = 4. If a > 0andb > 0, then and ln b x lim x! x a =0 [ln b n = o(n a )] lim x! x b e ax =0 [nb = o(e an )andn b = o(c n ), for c > ] COMP3600/6466 Algorithms 08 Lecture lg ( ) = 5. lg ( ) = 6. (That is, lg = 6.) COMP3600/6466 Algorithms 08 Lecture 3 Iterated logarithm lg (0) n = n, lg () n =lgn, lg () n =lglgn, lg (3) n =lglglgn,... In general: lg (k+) n =lglg (k) n. Iterated logarithm function: lg n =min{i 0 : lg (i) n apple }. Example: Determine the value of lg 6. Now lg 6 is the smallest integer i such that lg (i) 6 apple. We have lg (0) 6 = 6 > lg () 6 =lg 6 = 6 > lg () 6 =lglg () 6 = lg 6 = lg 4 =4 > lg (3) 6 =lglg () 6 =lg4=lg = > lg (4) 6 =lglg (3) 6 =lg= It follows that lg 6 = 4. apple. COMP3600/6466 Algorithms 08 Lecture Comparison of growth rates Common functions fit in a hierarchy (growth rate increases from left to right: e.g. n lg n = O(n 3 )): n lg n... lg lg n... lg n... lg n... n... n... n lg n... n 3... n... n n... e n... 3 n... n!... n n... Most of these facts we accept (and remember!) without proof; others we will prove: I We will use (without proof) that exponential functions grow faster than powers. I We do not prove that logarithmic functions grow slower than powers. I We however often show simpler relationships, for example, that higher powers grow faster than lower powers. I We will prove results like n lg n = o(n 3 ), using the fact that logarithms grow slower than powers. COMP3600/6466 Algorithms 08 Lecture 3

4 Working with asymptotic notations To simplify a function by using asymptotic notation: Take the fastest growing additive term and ignore its constant coe cient. Examples: 3n +5n += (n ) n + 4(n +lgn) n = (n 3 ) 4n +lnn n + = (n) n n + O(n 3 )= (n n ) 6 ln n + n 00 = (n 00 ) Sample proof O(g (n)) (g (n)) = O g (n).(g (n) assumed asymptotically positive) g (n) Let f (n) =O(g (n)), which means: There are constants n > 0, c > 0 such that 0 apple f (n) apple c g (n), for n n. Let f (n) = (g (n)), which means: There are constants n > 0, c > 0 such that 0 apple c g (n) apple f (n), for n n. We obtain 0 apple f (n) f (n) apple c g (n) f (n) for n max(n, n ). apple c g (n) c g (n) = c c g(n) g (n), 5n + (n) = (n ) (careful!) 3 Since c = c c and n 0 =max(n, n ) are positive constants, the statement follows. 5 COMP3600/6466 Algorithms 08 Lecture 3 3 COMP3600/6466 Algorithms 08 Lecture 3 5 Further asymptotic properties Solution to Exercise I O(g (n)+g (n)) = O(max{g (n), g (n)}) e.g., O(n 5 + log n) =O(n 5 ) I O(g (n)) + O(g (n)) = O(max{g (n), g (n)}) e.g., O(n 3 )+O(n )=O(n 3 ) I O(g (n)) O(g (n)) = O(g (n) g (n)) e.g., O n O(log n) =O log n n (g (n)) I O(g (n)) = g (n). e.g., () g (n) O(n) = n O(g (n)) I (g (n)) = O g (n). e.g., O(n ) g (n) (n) = O(n) Exercise: Try proving some of the above. Exercise: Show that O(n )=O(n) is not valid. If O(n )=O(n) were true, then, for any choice f (n) from the set O(n ), there would be a function g(n) ino(n) suchthat f (n) =g(n). In other words, O(n ) would be a subset of O(n). (Here, it is precise to use subset, because on both sides of the equation, we have well-defined sets.) However, f (n) =n is a member of O(n ) but it is not a member of O(n). Thus, O(n ) is not a subset of O(n), that is, O(n ) 6= O(n). (Note that there are many other functions in O(n ) that are not in O(n).) COMP3600/6466 Algorithms 08 Lecture COMP3600/6466 Algorithms 08 Lecture 3 6 6

5 Solution to Exercise Exercise: Prove that n + n O(ln n) =O(n ln n). We have to prove that, for any choice f (n) from the set O(ln n), there is a function g(n) ino(n ln n) suchthat n + n f (n) =g(n), that is, n + n f (n) is an element of O(n ln n). Let f (n) be an arbitrary element of O(ln n). This means that, there is a constant c > 0 such that 0 apple f (n) apple c ln n and so 0 apple n f (n) apple cn ln n, forallsu ciently large values of n. Thus 0 apple n+n f (n) apple n+c n ln n apple n ln n+c n ln n apple (c+) n ln n, for su ciently large n. By the definition of O, thismeansthat n + n f (n) =O(n ln n), as required. 7 COMP3600/6466 Algorithms 08 Lecture 3 7 Solution to Exercise Exercise: Prove that n 6=!(n ). Suppose that n =!(n ). By the definition of!-notation, for any constant c > 0, we have 0 apple cn < n,forsu ciently large n. (*) However, for c =, this gives the false statement that 0 apple n < n for su ciently large n. That is, statement (*) is not true for all c > 0, which is a contradiction. Thus the assumption that n =!(n )isfalse. COMP3600/6466 Algorithms 08 Lecture 3 8 8