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Property testing
Property testing is a field of theoretical computer science, concerned with the design of super-fast algorithms for approximate decision making, where the decision refers to properties or parameters of huge objects.
A property testing algorithm for a decision problem is an algorithm whose query complexity (the number of queries made to its input) is much smaller than the instance size of the problem. Typically, property testing algorithms are used to determine whether some combinatorial structure S (such as a graph or a boolean function) satisfies some property P, or is "far" from having this property (meaning that an ε-fraction of the representation of S must be modified to make S satisfy P), using only a small number of "local" queries to the object.
For example, the following promise problem admits an algorithm whose query complexity is independent of the instance size (for an arbitrary constant ε > 0):
Property testing algorithms are central to the definition of probabilistically checkable proofs, as a probabilistically checkable proof is essentially a proof that can be verified by a property testing algorithm.
Formally, a property testing algorithm with query complexity q(n) and proximity parameter ε for a decision problem L is a randomized algorithm that, on input x (an instance of L) makes at most q(|x|) queries to x and behaves as follows:
Here, "x is ε-far from L" means that the Hamming distance between x and any string in L is at least ε |x|.
A property testing algorithm is said to have one-sided error if it satisfies the stronger condition that the accepting probability for instances x ∈ L is 1 instead of 2/3.
A property testing algorithm is said be non-adaptive if it performs all its queries before it "observes" any answers to previous queries. Such an algorithm can be viewed as operating in the following manner. First the algorithm receives its input. Before looking at the input, using its internal randomness, the algorithm decides which symbols of the input are to be queried. Next, the algorithm observes these symbols. Finally, without making any additional queries (but possibly using its randomness), the algorithm decides whether to accept or reject the input.
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Property testing AI simulator
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Property testing
Property testing is a field of theoretical computer science, concerned with the design of super-fast algorithms for approximate decision making, where the decision refers to properties or parameters of huge objects.
A property testing algorithm for a decision problem is an algorithm whose query complexity (the number of queries made to its input) is much smaller than the instance size of the problem. Typically, property testing algorithms are used to determine whether some combinatorial structure S (such as a graph or a boolean function) satisfies some property P, or is "far" from having this property (meaning that an ε-fraction of the representation of S must be modified to make S satisfy P), using only a small number of "local" queries to the object.
For example, the following promise problem admits an algorithm whose query complexity is independent of the instance size (for an arbitrary constant ε > 0):
Property testing algorithms are central to the definition of probabilistically checkable proofs, as a probabilistically checkable proof is essentially a proof that can be verified by a property testing algorithm.
Formally, a property testing algorithm with query complexity q(n) and proximity parameter ε for a decision problem L is a randomized algorithm that, on input x (an instance of L) makes at most q(|x|) queries to x and behaves as follows:
Here, "x is ε-far from L" means that the Hamming distance between x and any string in L is at least ε |x|.
A property testing algorithm is said to have one-sided error if it satisfies the stronger condition that the accepting probability for instances x ∈ L is 1 instead of 2/3.
A property testing algorithm is said be non-adaptive if it performs all its queries before it "observes" any answers to previous queries. Such an algorithm can be viewed as operating in the following manner. First the algorithm receives its input. Before looking at the input, using its internal randomness, the algorithm decides which symbols of the input are to be queried. Next, the algorithm observes these symbols. Finally, without making any additional queries (but possibly using its randomness), the algorithm decides whether to accept or reject the input.