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Describe an algorithm that can convert a NFA to an equivalent DFA,and analyze its time complexity
使用subset construction method
Algorithm to Convert an NFA to an Equivalent DFA and Its Time Complexity Analysis
2022-9-19 18分钟
Introduction
A Nondeterministic Finite Automaton (NFA) can be converted to an equivalent Deterministic Finite Automaton (DFA) using the subset construction algorithm, also known as the powerset construction. This algorithm systematically constructs a DFA that recognizes the same language as the given NFA by considering all possible subsets of NFA states.
Algorithm Description
Given an NFA \( N = (Q_N, \Sigma, \delta_N, q_{0N}, F_N) \), where:
- \( Q_N \): Set of NFA states.
- \( \Sigma \): Input alphabet.
- \( \delta_N \): Transition function \( \delta_N: Q_N \times \Sigma \rightarrow 2^{Q_N} \).
- \( q_{0N} \): Initial state.
- \( F_N \): Set of accepting (final) states.
We aim to construct a DFA \( D = (Q_D, \Sigma, \delta_D, q_{0D}, F_D) \), where:
- \( Q_D \): Set of DFA states (each state is a subset of \( Q_N \)).
- \( \delta_D \): Transition function \( \delta_D: Q_D \times \Sigma \rightarrow Q_D \).
- \( q_{0D} \): Initial state (subset of \( Q_N \)).
- \( F_D \): Set of accepting states.
Algorithm Steps
- Initialize the DFA
- Initial State: Start with the initial state \( q_{0D} \) of the DFA, which is the epsilon-closure of the NFA's initial state \( q_{0N} \): \[ q_{0D} = \epsilon\text{-closure}(\{q_{0N}\}) \]
- Epsilon-Closure: The epsilon-closure of a set of states \( S \) is the set of states reachable from any state in \( S \) via epsilon (empty string) transitions, including \( S \) itself.
- Construct the DFA States and Transitions
- Worklist Algorithm: Use a queue or stack to keep track of unprocessed DFA states.
- Initialize: Add \( q_{0D} \) to the worklist and \( Q_D \).
- Process States:
- While the worklist is not empty:
- Remove a state \( T \) from the worklist.
- For each input symbol \( a \in \Sigma \):
- Compute the set of NFA states \( U \) reachable from \( T \) on input \( a \): \[ U = \epsilon\text{-closure}\left( \bigcup_{q \in T} \delta_N(q, a) \right) \]
- Add New State:
- If \( U \) is not already in \( Q_D \):
- Add \( U \) to \( Q_D \).
- Add \( U \) to the worklist.
- Define Transition:
- Set \( \delta_D(T, a) = U \).
- Define Accepting States
- Accepting States: A DFA state \( T \) is accepting if it contains at least one accepting state of the NFA: \[ F_D = \{ T \in Q_D \mid T \cap F_N \neq \emptyset \} \]
Time Complexity Analysis
Let \( n = |Q_N| \) be the number of states in the NFA, and \( m = |\Sigma| \) be the size of the input alphabet.
- Number of DFA States
- Theoretical Upper Bound: The number of possible subsets of \( Q_N \) is \( 2^n \). Therefore, \( |Q_D| \leq 2^n \).
- Practical Observation: Not all subsets are reachable from the initial state, so the actual number of DFA states may be less.
- Processing Each DFA State
- For each DFA state \( T \) and input symbol \( a \), we compute the set \( U \) as described.
- Computational Steps:
- Transition Retrieval:
- For each state \( q \in T \), retrieve \( \delta_N(q, a) \).
- Set Union:
- Combine the resulting sets for all \( q \in T \).
- Epsilon-Closure:
- Compute the epsilon-closure of the combined set.
- Time per Transition:
- Retrieving transitions: \( O(n) \) (since \( |T| \leq n \)).
- Epsilon-closure computation: \( O(n^2) \) in the worst case (if the NFA has epsilon transitions forming a connected graph).
- Total per transition: \( O(n^2) \).
- Total Time Complexity
- Total Number of Transitions:
- Up to \( |Q_D| \times m \).
- In the worst case: \( 2^n \times m \).
- Total Computation Time:
- Time per transition \( \times \) total number of transitions.
- \( O(2^n \times m \times n^2) \).
- Simplified Complexity:
- \( O(m n^2 2^n) \).
- Space Complexity
- DFA States Storage:
- Up to \( 2^n \) states, each storing a subset of \( Q_N \).
- Transition Table:
- Up to \( 2^n \times m \) transitions.
- Total Space:
- \( O(2^n \times n) \) (for states) \( + \) \( O(2^n \times m) \) (for transitions).
- Combined: \( O(2^n (n + m)) \).
Conclusion
The subset construction algorithm effectively converts an NFA to an equivalent DFA by considering all possible combinations of NFA states. However, due to the exponential growth of possible state subsets, the algorithm has:
- Time Complexity: Exponential in the number of NFA states (\( O(2^n) \)).
- Space Complexity: Exponential in the number of NFA states (\( O(2^n) \)).
Despite the exponential worst-case complexity, this method is fundamental in automata theory and compiler design, and optimizations or alternative representations are often used in practical applications to mitigate the exponential growth.
Summary of the Algorithm Steps
- Initialization:
- Start with the epsilon-closure of the NFA's initial state.
- Initialize the DFA states and worklist.
- State Processing Loop:
- For each DFA state and input symbol:
- Compute the move and epsilon-closure to find the new DFA state.
- Add new DFA states to the worklist.
- Defining Accepting States:
- Any DFA state containing an NFA accepting state becomes an accepting state.
- Time Complexity:
- Exponential due to the potential \( 2^n \) number of DFA states.
Practical Notes
- Optimizations:
- In practice, many of the possible subsets are unreachable, so the actual number of DFA states may be much less than \( 2^n \).
- Techniques like memoization and state minimization can help reduce the number of states and transitions.
- Applications:
- Essential in lexical analysis, where regular expressions are converted to NFAs and then to DFAs for efficient pattern matching.
- Limitations:
- For NFAs with a large number of states, the subset construction may become computationally infeasible due to resource constraints.
Reference
- Automata Theory: The study of abstract machines and problems they can solve.
- Powerset Construction: Method of converting NFAs to DFAs by representing each DFA state as a set of NFA states.
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- 作者:fufu酱
- 链接:https://csfufu.life/article/125166b7-5648-809d-ad37-fbe9241ce60f
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