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INHERENT LIMITATIONS OF COMPUTER PROGRAMS CSci 4011.

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Presentation on theme: "INHERENT LIMITATIONS OF COMPUTER PROGRAMS CSci 4011."— Presentation transcript:

1 INHERENT LIMITATIONS OF COMPUTER PROGRAMS CSci 4011

2 WHEN A COMPUTER IS USEFUL…

3 ENCODING CHESS A 88 chess board has 64 squares and 32 pieces: Each square can have one of 13 values: 4 bits CHESS = { 〈 B 〉 | B is a chess board and white can force a win. } +1 bit encodes turn information.

4 CHESS IS DECIDABLE can_white_force_win(board, b_list): if board ∈ b_list: return NO # black can force stalemate if checkmate(black, board): return YES if checkmate(white, board): return NO if turn(board) == white: win = NO else: win = YES for m ∈ legal_moves(board): win_move = can_white_force_win(move(board,m), b_list+[board]) if turn(board) == white: win = win or win_move else: win = win and win_move return win

5 HOW LONG TO SAVE THE DAY? How many moves at any level? ≤ 376 W ≥ 9Q + 1K + 2B + 2R + 2N = 376 How many levels? D ≥ (64) 32 2 = ≈ ≤ So, at most, W D = 10 positions By remembering whether each board B ∈ CHESS, we can explore “only” D ≈ positions. Can we do better?

6 DIVIDING BUGS A circuit is a collection of boolean gates and inputs connected by wires: ∧ ⋁ ¬ ⋁ x0x0 x1x1 x2x2 It is satisfiable if some setting of inputs makes it output 1. It can be encoded by listing for the inputs and operation of each gate. CIRCUIT-SAT = { 〈 C 〉 | C is a satisfiable circuit }

7 CIRCUIT-SAT IS DECIDABLE is_satisfiable(C): for each input value x ∈ {0 n, 0 n-1 1, … 1 n }: if circuit_value(C,x)==1: return True else: return False How long to check the Pentium division circuit? 2 64-bit inputs: at most calls to circuit_value Around steps. Can we do better?

8 STABLE MARRIAGES A High School has N boys and N girls. Each has a ranked list of dates for the 1951 Senior Prom. Albert BobCharlie Alice Betty Carol B,C,A A,C,B A,B,C B,A,CC,A,BC,B,A An unstable couple prefer each other to their current dates. STABLE = { 〈 B,G 〉 | There is a pairing with no unstable couple}

9 COMPLEXITY THEORY Studies what can and can’t be computed under limited resources such as time, space, etc Today: Time complexity

10 DECISION VS SEARCH Decision ProblemsSearch Problems Is there a winning move for white? Find a winning move for white Is there an input to satisfy the circuit? Find a satisfying input. Is there a stable pairing?Find a stable pairing.

11 MEASURING TIME COMPLEXITY We measure time complexity by counting the elementary steps required for a machine to halt Consider the language A = { 0 k 1 k | k  0 } 1. Scan across the tape and reject if the string is not of the form 0 m 1 n 2. Repeat the following if both 0s and 1s remain on the tape: Scan across the tape, crossing off a single 0 and a single 1 3. If 0s remain after all 1s have been crossed off, or vice-versa, reject. Otherwise accept. 2k 2k 2 2k

12 Definition: Let M be a TM that halts on all inputs. The running time or time-complexity of M is the function ƒ : ℕ  ℕ, where ƒ(n) is the maximum number of steps that M uses on any input of length n.

13 ASYMPTOTIC ANALYSIS 5n 3 + 2n n + 6= O(n 3 )

14 Let f and g be two functions f, g : ℕ → ℝ +. We say that f(n) = O(g(n)) if positive integers c and n 0 exist so that for every integer n  n 0 f(n)  cg(n) When f(n) = O(g(n)), we say that g(n) is an asymptotic upper bound for f(n) BIG- O 5n 3 + 2n n + 6= O(n 3 ) If c = 6 and n 0 = 10, then 5n 3 + 2n n + 6  cn 3

15 3n log 2 n + 5n log 2 log 2 n 2n n n log 10 n 78 = O(n 4.1 ) = O(n log 2 n) = O(n log 10 n) log 10 n = log 2 n / log 2 10 O(n log 2 n) = O(n log 10 n) = O(n log n)

16 EXAMPLES Is f(n) = O(g(n)) or g(n) = O(f(n))? f(n)g(n) 4n 2 2n2n n log n n! n log log n (log n) log n

17 WHY USE BIG-O? Suppose there is a k-state TM for L that runs in time f(n). Then there is a O(k2 c )-state TM for L that runs in time f(n)/c:

18 Definition: TIME(t(n)) = { L | L is a language decided by a O(t(n)) time Turing Machine } A = { 0 k 1 k | k  0 }  TIME(n 2 ) CIRCUIT-SAT  TIME(2 n )

19 A = { 0 k 1 k | k  0 }  TIME(n log n) Cross off every other 0 and every other 1. If the # of 0s and 1s left on the tape is odd, reject x0x0x0x0x0x0xx1x1x1x1x1x1x xxx0xxx0xxx0xxxx1xxx1xxx1x xxxxxxx0xxxxxxxxxxxx1xxxxx xxxxxxxxxxxxxxxxxxxxxxxxxx

20 We can prove that a TM cannot decide A in less time than O(n log n)

21 Can A = { 0 k 1 k | k  0 } be decided in time O(n) with a two-tape TM? Scan all 0s and copy them to the second tape. Scan all 1s, crossing off a 0 from the second tape for each 1.

22 Different models of computation yield different running times for the same language!

23 Theorem: Let t(n) be a function such that t(n)  n. Then every t(n)-time multi-tape TM has an equivalent O(t(n) 2 ) single tape TM

24 SIMULATING MULTIPLE TAPES 1. “Format” tape. If a tape head goes off right end, insert blank. If tape head goes off left end, move back right. Scan left-to-right, finding current symbols Scan left-to-right, writing new symbols Scan left-to-right, moving each tape head: 2. For each move of the k-tape TM: L#100#□#□#R qiqi q i1 q i1□ q i1□□ q j101RSS L#100#0#□#R L#100#0#1#R ● q jRSS 100 ● ● ● qjqj

25 COUNTING STEPS 1. “Format” tape. If a head goes off right end, insert blank. If a head goes off left end, move back right. Scan left-to-right, finding current symbols Scan left-to-right, writing new symbols Scan left-to-right, moving each tape head: 2. For each move of the k-tape TM: O(n+k) steps Do t(n) times: 2t(n) steps 2kt(n) steps Total = t(n)((6+2k) t(n)) + O(n+k) = O(t 2 (n))


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