4. GREEDY ALGORITHMS I

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1 4. GREEDY ALGORITHMS I coin chnging intervl scheduling scheduling to minimize lteness optiml cching Lecture slides by Kevin Wyne Copyright 2005 Person-Addison Wesley Lst updted on Feb 17, :07 AM

2 4. GREEDY ALGORITHMS I coin chnging intervl scheduling scheduling to minimize lteness optiml cching

3 Coin chnging Gol. Given currency denomintions: 1, 5, 10, 25, 100, devise method to py mount to customer using fewest number of coins. Ex. 34. Cshier's lgorithm. At ech itertion, dd coin of the lrgest vlue tht does not tke us pst the mount to be pid. Ex. $

4 Cshier's lgorithm At ech itertion, dd coin of the lrgest vlue tht does not tke us pst the mount to be pid. CASHIERSALGORITHM(x, c1, c2,, cn) SORT n coin denomintions so tht c1 < c2 < < cn S φ WHILE x > 0 k lrgest coin denomintion ck such tht ck x IF no such k, RETURN "no solution" ELSE RETURN S x x ck S S { k } set of coins selected Q. Is cshier's lgorithm optiml? 4

5 Properties of optiml solution Property. Number of pennies 4. Pf. Replce 5 pennies with 1 nickel. Property. Number of nickels 1. Property. Number of qurters 3. Property. Number of nickels + number of dimes 2. Pf. Replce 3 dimes nd 0 nickels with 1 qurter nd 1 nickel; Replce 2 dimes nd 1 nickel with 1 qurter. Recll: t most 1 nickel. 5

6 Anlysis of cshier's lgorithm Theorem. Cshier's lgorithm is optiml for U.S. coins: 1, 5, 10, 25, 100. Pf. [by induction on x] Consider optiml wy to chnge c k x < c k+1 : greedy tkes coin k. We clim tht ny optiml solution must lso tke coin k. if not, it needs enough coins of type c 1,, c k 1 to dd up to x tble below indictes no optiml solution cn do this Problem reduces to coin-chnging x c k cents, which, by induction, is optimlly solved by cshier's lgorithm. k ck ll optiml solutions must stisfy mx vlue of coins c1, c2,, ck 1 in ny OPT 1 1 P N N + D Q no limit = = = 99 6

7 Cshier's lgorithm for other denomintions Q. Is cshier's lgorithm for ny set of denomintions? A. No. Consider U.S. postge: 1, 10, 21, 34, 70, 100, 350, 1225, Cshier's lgorithm: 140 = Optiml: 140 = A. No. It my not even led to fesible solution if c1 > 1: 7, 8, 9. Cshier's lgorithm: 15 = 9 +???. Optiml: 15 =

8 4. GREEDY ALGORITHMS I coin chnging intervl scheduling scheduling to minimize lteness optiml cching SECTION 4.1

9 Intervl scheduling Job j strts t s j nd finishes t f j. Two jobs comptible if they don't overlp. Gol: find mximum subset of mutully comptible jobs. b c d e jobs d nd g re incomptible f g h time 9

10 Intervl scheduling: greedy lgorithms Greedy templte. Consider jobs in some nturl order. Tke ech job provided it's comptible with the ones lredy tken. [Erliest strt time] Consider jobs in scending order of s j. [Erliest finish time] Consider jobs in scending order of f j. [Shortest intervl] Consider jobs in scending order of f j s j. [Fewest conflicts] For ech job j, count the number of conflicting jobs c j. Schedule in scending order of c j. 10

11 Intervl scheduling: greedy lgorithms Greedy templte. Consider jobs in some nturl order. Tke ech job provided it's comptible with the ones lredy tken. counterexmple for erliest strt time counterexmple for shortest intervl counterexmple for fewest conflicts 11

12 Intervl scheduling: erliest-finish-time-first lgorithm EARLIESTFINISHTIMEFIRST(n, s1, s2,, sn, f1, f2,, fn) SORT jobs by finish time so tht f1 f2 fn A φ FOR j = 1 TO n IF job j is comptible with A A A { j } RETURN A set of jobs selected Proposition. Cn implement erliest-finish-time first in O(n log n) time. Keep trck of job j* tht ws dded lst to A. Job j is comptible with A iff s j f j*. Sorting by finish time tkes O(n log n) time. 12

13 Intervl scheduling: nlysis of erliest-finish-time-first lgorithm Theorem. The erliest-finish-time-first lgorithm is optiml. Pf. [by contrdiction] Assume greedy is not optiml, nd let's see wht hppens. Let i 1, i 2,... i k denote set of jobs selected by greedy. Let j 1, j 2,... j m denote set of jobs in n optiml solution with i 1 = j 1, i 2 = j 2,..., i r = j r for the lrgest possible vlue of r. job i r+1 exists nd finishes before j r+1 Greedy: i 1 i 2 i r i r+1... i k OPT: j 1 j 2 j r j r+1... j m why not replce job j r+1 with job i r+1? 13

14 Intervl scheduling: nlysis of erliest-finish-time-first lgorithm Theorem. The erliest-finish-time first lgorithm is optiml. Pf. [by contrdiction] Assume greedy is not optiml, nd let's see wht hppens. Let i 1, i 2,... i k denote set of jobs selected by greedy. Let j 1, j 2,... j m denote set of jobs in n optiml solution with i 1 = j 1, i 2 = j 2,..., i r = j r for the lrgest possible vlue of r. job i r+1 exists nd finishes before j r+1 Greedy:... i 1 i 2 i r i r+1 i k OPT: j 1 j 2 j r i r+1... j m solution still fesible nd optiml (but contrdicts mximlity of r) 14

15 Intervl prtitioning Intervl prtitioning. Lecture j strts t s j nd finishes t f j. Gol: find minimum number of clssrooms to schedule ll lectures so tht no two lectures occur t the sme time in the sme room. Ex. This schedule uses 4 clssrooms to schedule 10 lectures. 4 e j 3 c d g 2 b h 1 f i 9 9: : : :30 1 1:30 2 2:30 3 3:30 4 4:30 time 15

16 Intervl prtitioning Intervl prtitioning. Lecture j strts t s j nd finishes t f j. Gol: find minimum number of clssrooms to schedule ll lectures so tht no two lectures occur t the sme time in the sme room. Ex. This schedule uses 3 clssrooms to schedule 10 lectures. 3 c d f j 2 b g i 1 e h 9 9: : : :30 1 1:30 2 2:30 3 3:30 4 4:30 time 16

17 Intervl prtitioning: greedy lgorithms Greedy templte. Consider lectures in some nturl order. Assign ech lecture to n vilble clssroom (which one?); llocte new clssroom if none re vilble. [Erliest strt time] Consider lectures in scending order of s j. [Erliest finish time] Consider lectures in scending order of f j. [Shortest intervl] Consider lectures in scending order of f j s j. [Fewest conflicts] For ech lecture j, count the number of conflicting lectures c j. Schedule in scending order of c j. 17

18 Intervl prtitioning: greedy lgorithms Greedy templte. Consider lectures in some nturl order. Assign ech lecture to n vilble clssroom (which one?); llocte new clssroom if none re vilble. counterexmple for erliest finish time counterexmple for shortest intervl counterexmple for fewest conflicts

19 Intervl prtitioning: erliest-strt-time-first lgorithm EARLIESTSTARTTIMEFIRST(n, s1, s2,, sn, f1, f2,, fn) SORT lectures by strt time so tht s1 s2 sn. d 0 FOR j = 1 TO n IF lecture j is comptible with some clssroom Schedule lecture j in ny such clssroom k. ELSE number of llocted clssrooms Allocte new clssroom d + 1. Schedule lecture j in clssroom d + 1. d d + 1 RETURN schedule. 19

20 Intervl prtitioning: erliest-strt-time-first lgorithm Proposition. The erliest-strt-time-first lgorithm cn be implemented in O(n log n) time. Pf. Store clssrooms in priority queue (key = finish time of its lst lecture). To determine whether lecture j is comptible with some clssroom, compre sj to key of min clssroom k in priority queue. To dd lecture j to clssroom k, increse key of clssroom k to f j. Totl number of priority queue opertions is O(n). Sorting by strt time tkes O(n log n) time. Remrk. This implementtion chooses the clssroom k whose finish time of its lst lecture is the erliest. 20

21 Intervl prtitioning: lower bound on optiml solution Def. The depth of set of open intervls is the mximum number tht contin ny given time. Key observtion. Number of clssrooms needed depth. Q. Does number of clssrooms needed lwys equl depth? A. Yes! Moreover, erliest-strt-time-first lgorithm finds one. depth = 3 3 c d f j 2 b g i 1 e h 9 9: : : :30 1 1:30 2 2:30 3 3:30 4 4:30 time 21

22 Intervl prtitioning: nlysis of erliest-strt-time-first lgorithm Observtion. The erliest-strt-time first lgorithm never schedules two incomptible lectures in the sme clssroom. Theorem. Erliest-strt-time-first lgorithm is optiml. Pf. Let d = number of clssrooms tht the lgorithm lloctes. Clssroom d is opened becuse we needed to schedule lecture, sy j, tht is incomptible with ll d 1 other clssrooms. These d lectures ech end fter s j. Since we sorted by strt time, ll these incomptibilities re cused by lectures tht strt no lter thn s j. Thus, we hve d lectures overlpping t time s j + ε. Key observtion ll schedules use d clssrooms. 22

23 4. GREEDY ALGORITHMS I coin chnging intervl scheduling scheduling to minimize lteness optiml cching SECTION 4.2

24 Scheduling to minimizing lteness Minimizing lteness problem. Single resource processes one job t time. Job j requires t j units of processing time nd is due t time d j. If j strts t time s j, it finishes t time f j = s j + t j. Lteness: j = mx { 0, f j d j }. Gol: schedule ll jobs to minimize mximum lteness L = mx j j t j d j lteness = 2 lteness = 0 mx lteness = 6 d 3 = 9 d 2 = 8 d 6 = 15 d 1 = 6 d 5 = 14 d 4 =

25 Minimizing lteness: greedy lgorithms Greedy templte. Schedule jobs ccording to some nturl order. [Shortest processing time first] Schedule jobs in scending order of processing time t j. [Erliest dedline first] Schedule jobs in scending order of dedline d j. [Smllest slck] Schedule jobs in scending order of slck d j t j. 25

26 Minimizing lteness: greedy lgorithms Greedy templte. Schedule jobs ccording to some nturl order. [Shortest processing time first] Schedule jobs in scending order of processing time t j. 1 2 counterexmple tj dj [Smllest slck] Schedule jobs in scending order of slck d j t j. 1 2 tj dj counterexmple 26

27 Minimizing lteness: erliest dedline first EARLIESTDEADLINEFIRST(n, t1, t2,, tn, d1, d2,, dn) SORT n jobs so tht d1 d2 dn. t 0 FOR j = 1 TO n Assign job j to intervl [t, t +tj]. sj t ; fj t + tj t t + tj RETURN intervls [s1, f1], [s2, f2],, [sn, fn]. mx lteness = 1 d 1 = 6 d 2 = 8 d 3 = 9 d 4 = 9 d 5 = 14 d 6 =

28 Minimizing lteness: no idle time Observtion 1. There exists n optiml schedule with no idle time. d = 4 d = d = d = 4 d = 6 d = Observtion 2. The erliest-dedline-first schedule hs no idle time. 28

29 Minimizing lteness: inversions Def. Given schedule S, n inversion is pir of jobs i nd j such tht: i < j but j scheduled before i. inversion f i j i [ s before, we ssume jobs re numbered so tht d 1 d 2 d n ] Observtion 3. The erliest-dedline-first schedule hs no inversions. Observtion 4. If schedule (with no idle time) hs n inversion, it hs one with pir of inverted jobs scheduled consecutively. 29

30 Minimizing lteness: inversions Def. Given schedule S, n inversion is pir of jobs i nd j such tht: i < j but j scheduled before i. inversion f i before swp j i fter swp i j f ' j Clim. Swpping two djcent, inverted jobs reduces the number of inversions by one nd does not increse the mx lteness. Pf. Let be the lteness before the swp, nd let ' be it fterwrds. ' k = k for ll k i, j. ' i i. If job j is lte, ' j = f 'j dj (definition) = f i d j ( j now finishes t time f i ) f i d i (since i nd j inverted) i. (definition) 30

31 Minimizing lteness: nlysis of erliest-dedline-first lgorithm Theorem. The erliest-dedline-first schedule S is optiml. Pf. [by contrdiction] Define S* to be n optiml schedule tht hs the fewest number of inversions, nd let's see wht hppens. Cn ssume S* hs no idle time. If S* hs no inversions, then S = S*. If S* hs n inversion, let i j be n djcent inversion. Swpping i nd j does not increse the mx lteness strictly decreses the number of inversions This contrdicts definition of S* 31

32 Greedy nlysis strtegies Greedy lgorithm stys hed. Show tht fter ech step of the greedy lgorithm, its solution is t lest s good s ny other lgorithm's. Structurl. Discover simple "structurl" bound sserting tht every possible solution must hve certin vlue. Then show tht your lgorithm lwys chieves this bound. Exchnge rgument. Grdully trnsform ny solution to the one found by the greedy lgorithm without hurting its qulity. Other greedy lgorithms. Gle-Shpley, Kruskl, Prim, Dijkstr, Huffmn, 32

33 4. GREEDY ALGORITHMS I coin chnging intervl scheduling scheduling to minimize lteness optiml cching SECTION 4.3

34 Optiml offline cching Cching. Cche with cpcity to store k items. Sequence of m item requests d 1, d 2,, d m. Cche hit: item lredy in cche when requested. Cche miss: item not lredy in cche when requested: must bring requested item into cche, nd evict some existing item, if full. Gol. Eviction schedule tht minimizes number of evictions. Ex. k = 2, initil cche = b, requests:, b, c, b, c,,. Optiml eviction schedule. 2 evictions. b c c b b b cche miss (eviction) b c b c c b b b b requests cche 34

35 Optiml offline cching: greedy lgorithms LIFO / FIFO. Evict element brought in most (est) recently. LRU. Evict element whose most recent ccess ws erliest. LFU. Evict element tht ws lest frequently requested. previous queries w x y z FIFO: eject d w x d z LRU: eject d w x d z b b x d z c b c d z current cche e b c d e LIFO: eject e cche miss (which item to eject?) g b e d future queries 35

36 Optiml offline cching: frthest-in-future (clirvoynt lgorithm) Frthest-in-future. Evict item in the cche tht is not requested until frthest in the future. current cche cche miss (which item to eject?) f b c e g b e b c d e d FF: eject d future queries Theorem. [Bélády 1966] FF is optiml eviction schedule. Pf. Algorithm nd theorem re intuitive; proof is subtle. 36

37 Reduced eviction schedules Def. A reduced schedule is schedule tht only inserts n item into the cche in step in which tht item is requested. b c item inserted when not requested b c x c b c c d c c b c d d b d d c c b d c b x b b d b c c b c c b b c b c b c b c n unreduced schedule reduced schedule 37

38 Reduced eviction schedules Clim. Given ny unreduced schedule S, cn trnsform it into reduced schedule S' with no more evictions. Pf. [by induction on number of unreduced items] Suppose S brings d into the cche t time t, without request. Let c be the item S evicts when it brings d into the cche. Cse 1: d evicted t time t', before next request for d. unreduced schedule S.. c.. c Cse 1 S'.. c.. c.. c.. c time t time t' d e.. d.. d.. d.. e.. e d enters cche without request d evicted before next request d e.. c.. c.. c.. e.. e might s well leve c in cche 38

39 Reduced eviction schedules Clim. Given ny unreduced schedule S, cn trnsform it into reduced schedule S' with no more evictions. Pf. [by induction on number of unreduced items] Suppose S brings d into the cche t time t, without request. Let c be the item S evicts when it brings d into the cche. Cse 1: d evicted t time t', before next request for d. Cse 2: d requested t time t' before d is evicted. unreduced schedule S.. c.. c Cse 2 S'.. c.. c.. c.. c time t d time t' d.. d.. d.. d.. d.. d d enters cche without request d requested before d evicted d d.. c.. c.. c.. d.. d might s well leve c in cche until d is requested 39

40 Frthest-in-future: nlysis Theorem. FF is optiml eviction lgorithm. Pf. Follows directly from invrint. Invrint. There exists n optiml reduced schedule S tht mkes the sme eviction schedule s S FF through the first j requests. Pf. [by induction on j] Let S be reduced schedule tht stisfies invrint through j requests. We produce S' tht stisfies invrint fter j + 1 requests. Consider (j + 1)st request d = d j+1. Since S nd S FF hve greed up until now, they hve the sme cche contents before request j + 1. Cse 1: (d is lredy in the cche). S' = S stisfies invrint. Cse 2: (d is not in the cche nd S nd S FF evict the sme element). S' = S stisfies invrint. 40

41 Frthest-in-future: nlysis Pf. [continued] Cse 3: (d is not in the cche; S FF evicts e; S evicts f e). begin construction of S' from S by evicting e insted of f sme e f j sme e f S sme e d j+1 S' sme d f now S' grees with S FF on first j + 1 requests; we show tht hving element f in cche is no worse thn hving element e let S' behve the sme s S until S' is forced to tke different ction (becuse either S evicts e; or becuse either e or f is requested) 41

42 Frthest-in-future: nlysis Let j' be the first time fter j + 1 tht S' must tke different ction from S, nd let g be item requested t time j'. involves e or f (or both) sme e j' sme f S S' Cse 3: g = e. Cn't hppen with FF since there must be request for f before e. Cse 3b: g = f. Element f cn't be in cche of S, so let e' be the element tht S evicts. if e' = e, S' ccesses f from cche; now S nd S' hve sme cche if e' e, we mke S' evict e' nd brings e into the cche; now S nd S' hve the sme cche We let S' behve exctly like S for remining requests. S' is no longer reduced, but cn be trnsformed into reduced schedule tht grees with SFF through step j+1 42

43 Frthest-in-future: nlysis Let j' be the first time fter j + 1 tht S' must tke different ction from S, nd let g be item requested t time j'. involves e or f (or both) sme e j' sme f S S' otherwise S' could hve tke the sme ction Cse 3c: g e, f. S evicts e. Mke S' evict f. sme g j' sme g S S' Now S nd S' hve the sme cche. (nd we let S' behve exctly like S for the remining requests) 43

44 Cching perspective Online vs. offline lgorithms. Offline: full sequence of requests is known priori. Online (relity): requests re not known in dvnce. Cching is mong most fundmentl online problems in CS. LIFO. Evict pge brought in most recently. LRU. Evict pge whose most recent ccess ws erliest. FIF with direction of time reversed! Theorem. FF is optiml offline eviction lgorithm. Provides bsis for understnding nd nlyzing online lgorithms. LRU is k-competitive. [Section 13.8] LIFO is rbitrrily bd. 44

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