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Secretary problem. Graphs of probabilities of getting the best candidate (red circles) from n applications, and k / n (blue crosses) where k is the sample size. The secretary problem demonstrates a scenario involving optimal stopping theory [1] [2] that is studied extensively in the fields of applied probability, statistics, and decision theory.
Curve fitting. Fitting of a noisy curve by an asymmetrical peak model, with an iterative process ( Gauss–Newton algorithm with variable damping factor α). Curve fitting [1] [2] is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, [3] possibly subject to constraints.
e. Bayes consistent loss functions: Zero-one loss (gray), Savage loss (green), Logistic loss (orange), Exponential loss (purple), Tangent loss (brown), Square loss (blue) In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of ...
Generic-case complexity is a way of measuring the complexity of a computational problem by neglecting a small set of unrepresentative inputs and considering worst-case complexity on the rest. Small is defined in terms of asymptotic density. The apparent efficacy of generic case complexity is because for a wide variety of concrete computational ...
EXPSPACE. In computational complexity theory, EXPSPACE is the set of all decision problems solvable by a deterministic Turing machine in exponential space, i.e., in space, where is a polynomial function of . Some authors restrict to be a linear function, but most authors instead call the resulting class ESPACE.
This extended exponential function still satisfies the exponential identity, and is commonly used for defining exponentiation for complex base and exponent. Powers via logarithms. The definition of e x as the exponential function allows defining b x for every positive real numbers b, in terms of exponential and logarithm function.
An example is the partition problem. Both weak NP-hardness and weak polynomial-time correspond to encoding the input agents in binary coding. If a problem is strongly NP-hard, then it does not even have a pseudo-polynomial time algorithm. It also does not have a fully-polynomial time approximation scheme. An example is the 3-partition problem.
Use. An ansatz is the establishment of the starting equation (s), the theorem (s), or the value (s) describing a mathematical or physical problem or solution. It typically provides an initial estimate or framework to the solution of a mathematical problem, [3] and can also take into consideration the boundary conditions (in fact, an ansatz is ...
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