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1 Preview Greed Algorithms Greed Algorithms Coin Chnge Huffmn Code Greed lgorithms end to e simple nd strightforwrd. Are often used to solve optimiztion prolems. Alws mke the choice tht looks est t the moment, i.e., locll optiml choice Greed lgorithms mke locll optiml choices hoping tht the will led to gloll optiml solution. Of course, this is not lws true ut there re prolems for which it is. 1 2 Coin Chnge Coin Chnge Prolem Mke chnge for given mount using the smllest possile numer of coins. he ville coins re: dollrs (100 cents) qurters ( cents) dimes (10 cents) nickels (5 cents) pennies (1 cent) Greed strteg Sum = 0 Repetedl choose the lrgest coin k such tht Sum + k otl chnge mount 3 4 Coin Chnge Coin Chnge MAKE-CHANGE (n) C {100,, 10, 5, 1} // constnt. Sol {}; // set tht will hold the solution set. Sum 0 sum of item in solution set WHILE sum n = lrgest item in set C such tht sum + n IF no such item eists HEN REURN "No Solution" Sol Sol + {vlue of } sum sum + REURN Sol Greed methods do not work in ll cses!! For emple: Coin set = {8, 5, 1}; Chnge = 10. Greed Solution = {8, 1, 1} Optiml Solution (i.e., fewest # of coins) = {5, 5} 5 6 1

2 n ctivities require eclusive use of common resource. For emple, scheduling the use of clssroom. Set of ctivities S = { 1,..., n }. i needs the resource during the period [s i, f i ), which is the hlf-open intervl where s i = strt time nd f i = finish time. Gol: Select the lrgest possile set of non-overlpping ctivities. Alterntive prolems could hve different ojectives, e.g.: Schedule room for longest time. Mimize income rentl fees. 7 8 Consider some set of ctivities S sorted finish time: I S i F i One mimum-size mutull comptile set is { 1, 3, 6, 8 }. his set is not unique: 2, 5, 7, 9 lso works Generl Prolem Description Input: set of ctivities with strting times nd finish times. he ctivities re sorted finish time. Output: mimum size suset of comptile ctivities which could shre n ctivit room. Assume S set of ctivities S = { 1, 2,, n } Ech ctivit i hs strt time s i nd finishing time f i where 0 s i < f i < he set of ctivities S is sorted finishing time wo ctivities i nd j re comptile with ech other if their ctivtion times do not overlp f i s j or f j s i

3 (A Dnmic Progrmming Approch) 0 ime We cn use dnmic progrmming pproch s i i f i s i : strting time of ctivit i s k k Comptile ctivities: etween i nd j etween i nd l etween k nd j etween k nd l f k s j s l j f j l f l f i : finish time of ctivit i Let S ij denote the set of ctivities k comptile with ctivit i nd j. { S : f s f s } S ij k i k k j 13 (A Dnmic Progrmming Approch) (A Dnmic Progrmming Approch) f i 1 2 Some possile S ij re { 2, 4, 7 } { 2, 5, 7 } { 3, 5, 7 } s j Let c[i, j] e numer of ctivities in mimum-size of mutull comptile ctivities in S ij. We hve c[i, j] = 0 whenever S ij =. If k is used for mimum-size of mutull comptile ctivities of S ij, we lso use mimum-size of mutull comptile ctivities for S ik nd S kj (A Dnmic Progrmming Approch) (A Dnmic Progrmming Approch) f i S ik s k f k S kj s j Now we cn write recursive eqution 1 2 Some possile S ij re { 2, 4, 7 } { 2, 5, 7 } { 3, 5, 7 } c i, j] i k [ m k j Sij ( c[ i, k] c[ k, j] 1) if S if S ij ij

4 (A Dnmic Progrmming Approch) (A Greed Approch) When one considers the prolem of selecting ctivities one might hve sudden epiphn Dnmic progrmming lgorithm under construction (A Greed Approch) (A Recursive Greed Algorithm) When one considers the prolem of selecting ctivities one might hve sudden epiphn he ctivit we choose when solving suprolem is lws the one with the erliest finish time tht cn legll e scheduled. Wh? Becuse this greed choice leves the gretest possile leew for scheduling the remining ctivities. GREEDY-ACIVIY-SELECOR(s, f, i, n) { m = i + 1 while m <= n nd s[m] < f[i] // finds first ctivit in S i,n+1 m = m + 1; if m <= n return { m } GREEDY-ACIVIY-SELECOR(s, f, m, n) else return } (An Itertive Greed Algorithm) (A Greed Algorithm) IERAIVE-GREEDY-ACIVIY-SELECOR(s, f) { n = s.length A = { 1 } k = 1 for m = 2 to n { if s[m] f[k] { A = A { m } k = m } } return A he set of ctivities returned IERAIVE-GREEDY-ACIVIY-SELECOR(s,f) is the sme s the set returned GREEDY-ACIVIY-SELECOR(s,f,0,n) }

5 (A Greed Algorithm) Let s consider the following set of ctivities. Recll tht s i nd f i re the strt nd finish times for n ctivit i. (A Greed Algorithm) s i f i (A Greed Algorithm) (A Greed Algorithm) (A Greed Algorithm) (A Greed Algorithm)

6 Used for dt compression Binr chrcter codes cn e: Fied Length Codes ech chrcter is represented fied size unique code E.g., = 000, =001, c= 010, d = 011, Vrile Length Codes ech chrcter is represented vrile size unique code E.g., = 0, = 101, c= 100, d =111 Consider sving nd retrieving chrcters How do we encode chrcters into unique representtions? How do we decode encoded informtion? Prefi codes re one w to encode tet. A prefi code consists of set of words such tht no word in the set is prefi of nother word in the set. For emple: S = {0, 100, 101, 1100, 1101, 111} Huffmn used greed lgorithm for constructing n optiml prefi code clled the Huffmn code Let C: e set of n chrcters c C: e memer of C f(c): e the frequenc of chrcter c in file. he lgorithm uilds inr tree corresponding to the optiml prefi code in ottom-up mnner. he lgorithm strts with set of leves C which is sorted in incresing order of the frequenc f(c) Let Q = C, where Q is priorit queue (O(n) - uild hep) For i = 1 to C - 1 do Crete new node z. Get two minimum frequenc leves nd from Q where f[] f[] (O(2 log n) get m twice nd cll hepif twice) z s left child pointer (leled with 0) points to nd the right child pointer (leled with 1) points to f[z] = f[] + f[] Set Q = Q {, } {z} (O(log n)) End of for loop C: 12 B: 13 D: 16 C: 12 B: 13 D:

7 C: 12 B: 13 D: D: 16 C: 12 B: D: 16 D: 16 C: 12 B: 13 C: 12 B:

8 D: 16 C: 12 B:

9 A: 0 O (n log n) C: 100 B: 101 D: 111 F: 1100 E: Correctness of Huffmn Algorithm Lemm 1) Let C: set of n chrcters c C: memer of C f(c): frequenc of chrcter c in file., C hving the lowest frequencies. here eists n optiml prefi code for C where the code words for nd hve the sme length nd differ onl in the lst it. Correctness of Huffmn Algorithm Let e tree corresponding to prefi code. Let C: set of n chrcters c C: memer of C f(c): frequenc of chrcter c in file. d (c): the depth of c s lef in (numer of its need for encoding c). B(): numer of its required to encode file B ( ) f ( c) d ( c) cc Correctness of Huffmn Algorithm Correctness of Huffmn Algorithm Let, C (siling lef) nd d () nd d () re mimum nd f() f() in. Let f() nd f() is minimums such tht f() f() Also f() f() f() f() B( ) B( ') f ( c) d ( c) cc cc f ( ) d ( ) f ( ) d ( ) f ( ) d f ( ) d ( ) f ( ) d ( ) f ( ) d ( ) f ( ) d ( ) ( f ( ) f ( ))( d ( ) d ( )) 0 f ( c) d ' ' ( c) ( ) f ( ) d ' ( )

10 Correctness of Huffmn Algorithm Which mens tkes less its then for encoding file Also we cn compre nd nd conclude with tkes less its then for encoding file We cn conclude is n optiml tree in which nd pper s siling leves of mimum depth (lemm 1) Correctness of Huffmn Algorithm Lemm 2) Let C: set of n chrcters c C: memer of C f (c): frequenc of chrcter c in file., C hving the lowest frequencies. C = C {, } {z} f(z) = f() + f() : represent tree genertion optiml prefi code for the C hen the tree, otined from replcing the lef node for z with n internl node hving nd s children, presents n optiml prefi code for the lphet C. (see tet ook) 56 Correctness of Huffmn Algorithm heorem: Huffmn lgorithm produces n optiml prefi code Proof: from Lemm 1 nd Lemm 2, it is true

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