Lecture 2 Time and Space of DTM. Time of DTM Time M (x) = # of moves that DTM M takes on input x. Time M (x) < infinity iff x ε L(M).

Slides:



Advertisements
Similar presentations
Part VI NP-Hardness. Lecture 23 Whats NP? Hard Problems.
Advertisements

Lecture 3 Universal TM. Code of a DTM Consider a one-tape DTM M = (Q, Σ, Γ, δ, s). It can be encoded as follows: First, encode each state, each direction,
Lecture 16 Deterministic Turing Machine (DTM) Finite Control tape head.
Variants of Turing machines
1 Savitch and Immerman- Szelepcsènyi Theorems. 2 Space Compression  For every k-tape S(n) space bounded offline (with a separate read-only input tape)
Lecture 23 Space Complexity of DTM. Space Space M (x) = # of cell that M visits on the work (storage) tapes during the computation on input x. If M is.
Lecture 24 Time and Space of NTM. Time For a NDM M and an input x, Time M (x) = the minimum # of moves leading to accepting x if x ε L(M) = infinity if.
Resource-Bounded Computation Previously: can it be done? Now: how efficiently can it be done? Goal: conserve computational resources: Time, space, other.
CSCI 4325 / 6339 Theory of Computation Zhixiang Chen.
Umans Complexity Theory Lectures Lecture 2a: Reductions & Completeness.
Nathan Brunelle Department of Computer Science University of Virginia Theory of Computation CS3102 – Spring 2014 A tale.
Computability and Complexity 22-1 Computability and Complexity Andrei Bulatov Hierarchy Theorem.
P, NP, PS, and NPS By Muhannad Harrim. Class P P is the complexity class containing decision problems which can be solved by a Deterministic Turing machine.
Complexity ©D.Moshkovitz 1 Turing Machines. Complexity ©D.Moshkovitz 2 Motivation Our main goal in this course is to analyze problems and categorize them.
Complexity ©D.Moshkovits 1 Space Complexity Complexity ©D.Moshkovits 2 Motivation Complexity classes correspond to bounds on resources One such resource.
1 Slides: Asaf Shapira & Oded Schwartz; Sonny Ben-Shimon & Yaniv Nahum. Sonny Ben-Shimon & Yaniv Nahum. Notes: Leia Passoni, Reuben Sumner, Yoad Lustig.
CS151 Complexity Theory Lecture 2 April 3, Time and Space A motivating question: –Boolean formula with n nodes –evaluate using O(log n) space?
Umans Complexity Theory Lectures Lecture 2c: EXP Complete Problem: Padding and succinctness.
1 Slides: Asaf Shapira & Oded Schwartz; Sonny Ben-Shimon & Yaniv Nahum. Sonny Ben-Shimon & Yaniv Nahum. Notes: Leia Passoni, Reuben Sumner, Yoad Lustig.
CS 310 – Fall 2006 Pacific University CS310 Complexity Section 7.1 November 27, 2006.
CS151 Complexity Theory Lecture 2 April 1, CS151 Lecture 22 Time and Space A motivating question: –Boolean formula with n nodes –evaluate using.
Robbie Hott Department of Computer Science University of Virginia Theory of Computation CS3102.
Lecture 18 Various TMs. Allow the head not move Theorem. If the head is allowed to stay at the cell in each move, then every function computed by the.
CS 461 – Nov. 21 Sections 7.1 – 7.2 Measuring complexity Dividing decidable languages into complexity classes. Algorithm complexity depends on what kind.
Definition: Let M be a deterministic Turing Machine that halts on all inputs. Space Complexity of M is the function f:N  N, where f(n) is the maximum.
חישוביות וסיבוכיות Computability and Complexity Lecture 7 TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A AAAA.
Theory of Computing Lecture 15 MAS 714 Hartmut Klauck.
Ding-Zhu Du Office: ECSS 3-611, M 3:15-4:30 Lecture: ECSS 2.311, MW 12:30-1:45.
February 18, 2015CS21 Lecture 181 CS21 Decidability and Tractability Lecture 18 February 18, 2015.
Theory of Computing Lecture 17 MAS 714 Hartmut Klauck.
Theory of Computing Lecture 21 MAS 714 Hartmut Klauck.
CS848: Topics in Databases: Foundations of Query Optimization Topics covered  Review of complexity.
Measuring complexity Section 7.1 Giorgi Japaridze Theory of Computability.
Hierarchy theorems Section 9.1 Giorgi Japaridze Theory of Computability.
Lecture 22. Time of DTM. Time of DTM Time M (x) = # of moves that DTM M takes on input x. Time M (x) < infinity iff x ε L(M).
Umans Complexity Theory Lectures Lecture 1c: Robust Time & Space Classes.
Fall 2013 CMU CS Computational Complexity Lecture 2 Diagonalization, 9/12/2013.
Lecture 4 Hierarchy Theorem. Space Hierarchy Theorem.
Umans Complexity Theory Lectures Lecture 1b: Turing Machines & Halting Problem.
Theory of Computational Complexity Yuji Ishikawa Avis lab. M1.
1 8.4 Extensions to the Basic TM Extended TM’s to be studied: Multitape Turing machine Nondeterministic Turing machine The above extensions make no increase.
Recall last lecture and Nondeterministic TMs Ola Svensson.
Chapter 7 Introduction to Computational Complexity.
The Church-Turing Thesis
 2005 SDU Lecture14 Mapping Reducibility, Complexity.
Design and Analysis of Approximation Algorithms
Lecture 1-2 Time and Space of DTM
Time complexity Here we will consider elements of computational complexity theory – an investigation of the time (or other resources) required for solving.
Part VI NP-Hardness.
Umans Complexity Theory Lectures
HIERARCHY THEOREMS Hu Rui Prof. Takahashi laboratory
CSE322 The Chomsky Hierarchy
Intractable Problems Time-Bounded Turing Machines Classes P and NP
Intro to Theory of Computation
Chapter 14 Time Complexity.
CSE838 Lecture notes copy right: Moon Jung Chung
CS154, Lecture 12: Time Complexity
Part II Theory of Nondeterministic Computation
CSC 4170 Theory of Computation Time complexity Section 7.1.
Umans Complexity Theory Lectures
CS21 Decidability and Tractability
Theory of Computability
DSPACE Slides By: Alexander Eskin, Ilya Berdichevsky
CS6382 Theory of Computation
CSC 4170 Theory of Computation Time complexity Section 7.1.
CS151 Complexity Theory Lecture 5 April 16, 2019.
The Chomsky Hierarchy Costas Busch - LSU.
Intro to Theory of Computation
Lecture 1-2 Time and Space of DTM
Presentation transcript:

Lecture 2 Time and Space of DTM

Time of DTM Time M (x) = # of moves that DTM M takes on input x. Time M (x) < infinity iff x ε L(M).

Time Bound M is said to have a time bound t(n) if for every x with |x| < n, Time M (x) < max {n+1, t(n)}

Theorem For any multitape DTM M, there exists a one-tape DTM M’ to simulate M within time Time M’ (x) < c + (Time M (x)) c is a constant. 2

Complexity Class A language L has a (deterministic) time- complexity t(n) if there is a multitape DTM M accepting L, with time bound t(n). DTIME(t(n)) = {L | L has a time bound t(n)}

Model Multitape TM with write-only output.

Linear Speed Up Suppose t(n)/n → infinity as n → infinity. Then for any constant c > 0, DTIME(t(n)) = DTIME(ct(n))

1--m 3m Bee dance

Model Independent Classes

Space Space M (x) = total # of cells that M visits on all work (storage) tapes during the computation on input x. If M is a multitape DTM, then the work tapes do not include the input tape and the write-only output tape.

Space Bound A DTM with k work tapes is said to have a space bound s(n) if for any input x with |x| < n, Space M (x) < max{k, s(n)}.

Time and Space For any DTM with k work tapes, Space M (x) < k (Time M (x) + 1)

Complexity Classes A language L has a space complexity s(n) if it is accepted by a multitape with write- only output tape DTM with space bound s(n). DSPACE(s(n)) = {L | L has space complexity s(n)}

Tape Compression Theorem For any function s(n) and any constant c > 0, DSPACE(s(n)) = DSPACE(c·s(n))

Model Independent Classes P = U c>0 DTIME(n ) EXP = U c > 0 DTIME(2 ) EXPOLY = U c > 0 DTIME(2 ) PSPACE = U c > 0 DSPACE(n ) c cn n c c

Extended Church-Turing Thesis A function computable in polynomial time in any reasonable computational model using a reasonable time complexity measure is computable by a DTM in polynomial time.

P PSPACE

PSPACE EXPOLY

A, B P imply A U B P

A, B P imply AB P

L P implies L* P

All regular sets belong to P

Space Hierarchy Theorem

Space-constructible function s(n) is fully space-constructible if there exists a DTM M such that for sufficiently large n and any input x with |x|=n, Space M (x) = s(n).

Space Hierarchy If s 2 (n) is a fully space-constructible function, s 1 (n)/s 2 (n) → 0 as n → infinity, s 1 (n) > log n, then DSPACE(s 2 (n)) DSPACE(s 1 (n)) ≠ Φ

Time Hierarchy

Time-constructible function t(n) is fully time-constructible if there exists a DTM such that for sufficiently large n and any input x with |x|=n, Time M (x) = t(n).

Time Hierarchy If t 1 (n) > n+1, t 2 (n) is fully time-constructible, t 1 (n) log t 1 (n) /t 2 (n) → 0 as n → infinity, then DTIME(t 2 (n)) DTIME(t 1 (n)) ≠ Φ

P EXP

EXP ≠ PSAPACE

PSPACE≠EXP