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Abstraction may be exact or faithful with respect to a property if one can answer a question about the property equally well on the concrete or abstract model. For instance, if one wishes to know what the result of the evaluation of a mathematical expression involving only integers +, -, ×, is worth modulo n , then one needs only perform all ...
Early examples of these algorithms are primarily decrease and conquer – the original problem is successively broken down into single subproblems, and indeed can be solved iteratively. Binary search, a decrease-and-conquer algorithm where the subproblems are of roughly half the original size, has a long history.
The state of a deterministic finite automaton = (,,,,) is unreachable if no string in exists for which = (,).In this definition, is the set of states, is the set of input symbols, is the transition function (mapping a state and an input symbol to a set of states), is its extension to strings (also known as extended transition function), is the initial state, and is the set of accepting (also ...
The process of feature selection aims to find a suitable subset of the input variables (features, or attributes) for the task at hand.The three strategies are: the filter strategy (e.g., information gain), the wrapper strategy (e.g., accuracy-guided search), and the embedded strategy (features are added or removed while building the model based on prediction errors).
A particle swarm searching for the global minimum of a function. In computational science, particle swarm optimization (PSO) [1] is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
First, the question is asked on the given formula Φ. If the answer is "no", the formula is unsatisfiable. Otherwise, the question is asked on the partly instantiated formula Φ{x 1 =TRUE}, that is, Φ with the first variable x 1 replaced by TRUE, and simplified accordingly. If the answer is "yes", then x 1 =TRUE, otherwise x 1 =FALSE. Values ...
Python supports normal floating point numbers, which are created when a dot is used in a literal (e.g. 1.1), when an integer and a floating point number are used in an expression, or as a result of some mathematical operations ("true division" via the / operator, or exponentiation with a negative exponent).
In the worst case, i = 1 or i = n − 2 at each recursive invocation yields a running time of O(n 2). In the best case, i = n / 2 or i = n ± 1 / 2 at each recursive invocation yields a running time of O(n log n). Using (fully or semi-) dynamic convex hull data structures, the simplification performed by the algorithm can be ...