In computer science, deterministic algorithm is an algorithm which, given a particular input, always produces the same result. This is used to increase the efficiency of machines.
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A deterministic algorithm is a step-by-step procedure that always produces the same output for a given input. It follows a predictable sequence of steps to solve a problem. On the other hand, a non-deterministic algorithm may produce different outputs for the same input due to randomness or non-deterministic choices made during its execution. This makes non-deterministic algorithms harder to predict and analyze compared to deterministic algorithms.
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Algorithm is deterministic if for a given input the output generated is same for a function. A mathematical function is deterministic. Hence the state is known at every step of the algorithm.Algorithm is non deterministic if there are more than one path the algorithm can take. Due to this, one cannot determine the next state of the machine running the algorithm. Example would be a random function.FYI,Non deterministic machines that can't solve problems in polynomial time are NP. Hence finding a solution to an NP problem is hard but verifying it can be done in polynomial time. Hope this helps.Pl correct me if I am wrong here.Thank you.Sharada
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P is the class of problems for which there is a deterministic polynomial time algorithm which computes a solution to the problem. NP is the class of problems where there is a nondeterministic algorithm which computes a solution to the problem, but no known deterministic polynomial time solution
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Boorapa Chodchoey has written:
'Optimal deterministic algorithm for the simple symmetric hypotheses testing problem'
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Deterministic and non-deterministic loops
A deterministic loop is predictable. The number of iterations of such a loop are known in advance, even before the loop has started. Most counting loops are deterministic. Before they start, we can say how many times they will execute.
A non-deterministic loop is not easily predicted. A loop that is driven by the response of a user is not deterministic, because we cannot predict the response of the user. Non-deterministic loops usually are controlled by a boolean and the number of iterations is not known in advance.
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non-deterministic means not predicable, hence non-deterministic finalization means the finalization (removing objects from memory) cannot be determined when that will happen
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Deterministic systems in which the output can be predicted with 100 percent certainty
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No, not every deterministic context-free language is regular. While regular languages are a subset of deterministic context-free languages, there are deterministic context-free languages that are not regular. This is because deterministic context-free languages can include more complex structures that cannot be captured by regular expressions.
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A deterministic Turing machine follows a single path of computation based on the input, while a non-deterministic Turing machine can explore multiple paths simultaneously. This means that non-deterministic machines have the potential to solve problems faster, but determining the correct path can be more complex.
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DFA - deterministic finite automata
NFA - non-deterministic finite automata
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A deterministic Turing machine follows a single path of computation based on its input, while a non-deterministic Turing machine can explore multiple paths simultaneously. This allows non-deterministic machines to potentially solve problems faster, but their solutions may not always be correct. Deterministic machines are more reliable but may take longer to solve certain problems.
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NFA - Non-deterministic Finite Automaton, aka NFSM (Non-deterministic Finite State Machine)
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Wikipedia gives this definition:
An algorithm is a finite sequence of well-defined, computer-implementable instructions, typically to solve a class of problems or to perform a computation.
An algorithm, literally, is a method for computing by which a desired result of a computation can be achieved. Currently, the widely accepted professional definition of an algorithm is: an algorithm is a set of feasible, deterministic, and finite rules for model analysis. In layman's terms, an algorithm can also be understood as a problem-solving step, consisting of some basic operations and a prescribed sequence. But from the point of view of computer programming, the algorithm consists of a series of instructions to solve the problem and can obtain effective output results in a limited time according to the normative input. Algorithms represent a strategic mechanism for describing problem solving in a systematic way.
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An algorithm is a step-by-step procedure for solving a problem, typically involving a finite number of steps. Heuristics, on the other hand, are general problem-solving strategies that may not guarantee a correct solution but can often lead to a quicker or simpler resolution. Algorithms are precise and deterministic, while heuristics are more flexible and open to interpretation.
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No, not all deterministic finite automata (DFAs) are also non-deterministic finite automata (NFAs). DFAs have a single unique transition for each input symbol, while NFAs can have multiple transitions for the same input symbol.
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It is likely to mean deterministic. It means that the outcome of an event is known and not subject to probability.
It is likely to mean deterministic. It means that the outcome of an event is known and not subject to probability.
It is likely to mean deterministic. It means that the outcome of an event is known and not subject to probability.
It is likely to mean deterministic. It means that the outcome of an event is known and not subject to probability.
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A deterministic MAC protocol gives guarantees on message delay and channel throughput. Schedule based MAC protocols, based on time synchronization among nodes, are currently used to implement deterministic MAC protocols.
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In general, finite state machines can model regular grammars.
Deterministic finite automata can represent deterministic context-free grammars.
Non-deterministic finite automata can represent context-free grammars.
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A non-deterministic Turing machine can explore multiple paths simultaneously, potentially leading to faster computation for certain problems. This makes it more powerful than a deterministic Turing machine in terms of computational speed. However, the non-deterministic machine's complexity is higher due to the need to consider all possible paths, which can make it harder to analyze and understand its behavior.
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Yes, it is possible to show that all deterministic finite automata (DFA) are decidable.
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Quantum mechanics is not deterministic, meaning that it does not predict outcomes with certainty. Instead, it deals with probabilities and uncertainties at the microscopic level of particles.
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A deterministic Finite Automata)DFA will have a single possible output for a given input.The answer is deterministic because you can always feel what the output will be.
A (Nondeterministic Finite Automata)NFA will have at least one input which will cause a "choice" to be made during a state transition,unlike a (deterministic Finite Automata)DFA one input can cause multiple outputs for a given (Nondeterministic Finite Automata)NFA.
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A deterministic finite automaton will have a single possible output for a given input. The answer is deterministic because you can always tell what the output will be.
A nondeterministic finite automaton will have at least one input which will cause a "choice" to be made during a state transition. Unlike a DFA, one input can cause multiple outputs for a given NFA.
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The Recursive least squares RLS adaptive filter is an algorithm which recursively finds the filter coefficients that minimize a weighted linear least squares cost function relating to the input signals. This is in contrast to other algorithms such as the least mean squares LMS that aim to reduce the mean square error. In the derivation of the RLS, the input signals are considered deterministic, while for the LMS and similar algorithm they are considered stochastic. Compared to most of its competitors, the RLS exhibits extremely fast convergence. However, this benefit comes at the cost of high computational complexity.
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The Recursive least squares RLS adaptive filter is an algorithm which recursively finds the filter coefficients that minimize a weighted linear least squares cost function relating to the input signals. This is in contrast to other algorithms such as the least mean squares LMS that aim to reduce the mean square error. In the derivation of the RLS, the input signals are considered deterministic, while for the LMS and similar algorithm they are considered stochastic. Compared to most of its competitors, the RLS exhibits extremely fast convergence. However, this benefit comes at the cost of high computational complexity.
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What is FIFO algorithm?
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DFA - Deterministic Finite Automata
NFA - Non-Deterministic Finite Automata
Both DFAs and NFAs are abstract machines which can be used to describe languages.
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In my understanding the probabilistic is a system that you can predict but no 100% like deterministic system. In other words the result is randomness i.e it can have many different results instead of single results.
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Here is the algorithm of the algorithm to write an algorithm to access a pointer in a variable. Algorithmically.
name_of_the_structure dot name_of_the _field,
eg:
mystruct.pointerfield
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- non-deterministic
- less overhead
- collisions exist
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types technology using determnistic MAC protocol and Non Deterministic MAC protocol?
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Black and White bakery algorithm is more efficient.
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Complexity of an algorithm is a measure of how long an algorithm would take to complete given
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Yes, it is possible to demonstrate that all deterministic finite automata (DFA) are in the complexity class P.
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An algorithm is a series of steps leading to a result. A flowchart can be a graphical representation of the algorithm.
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what is algorithm and its use there and analyze an algorithm
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By preparing test cases we can test an algorithm. The algorithm is tested with each test case.
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The random function generates a random number between 0 (inclusive) and 1 (exclusive) each time it is called. It uses a mathematical algorithm to produce pseudorandom numbers, which appear to be random but are actually generated using a deterministic process. This function is commonly used in programming to introduce variability and unpredictability.
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- a problem in NP means that it can be solved in polynomial time with a non-deterministic turing machine
- a problem that is NP-hard means that all problems in NP are "easier" than this problem
- a problem that is NP-complete means that it is in NP and it is NP-hard
example - Hamiltonian path in a graph:
The problem is: given a graph as input, an algorithm must say whether there is a hamiltonian path in it or not.
in NP: here is an algorithm that works in polynomial time on a non-deterministic turing machine:
guess a path in the graph. Check that it is really a hamiltonian path.
NP-hard: we use reduction from a problem that is NP-comlete (SAT for example). Given an input for the other problem we construct a graph for the hamiltonian-path problem. The graph should have a path iff the original problem should return "true".
Therefore, if there is an algorithm that executes in polynomial time, we solve all the problems in NP in polynomial time.j
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A deterministic system is one that produces predictable set of outputs given a set of specific input parameters. The outputs of a probabilistic system, on the other hand, always vary.
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Yes, because the deterministic approach is based on apparent facts that provide a reason for the result. It is a relief to know that the effect was not caused by the individual.
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evaluation iz same as the testing of an algorithm. it mainly refers to the finding of errors by processing an algorithm..
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