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There are two main streams — one focuses on maximization problems from contexts like economics, using the terms action, reward, value, and calling the discount factor β or γ, while the other focuses on minimization problems from engineering and navigation [citation needed], using the terms control, cost, cost-to-go, and calling the discount ...
A loose rule of thumb dictates that stiff differential equations require the use of implicit schemes, whereas non-stiff problems can be solved more efficiently with explicit schemes. The so-called general linear methods (GLMs) are a generalization of the above two large classes of methods. [12]
In applied mathematics, discontinuous Galerkin methods (DG methods) form a class of numerical methods for solving differential equations.They combine features of the finite element and the finite volume framework and have been successfully applied to hyperbolic, elliptic, parabolic and mixed form problems arising from a wide range of applications.
The mesh is an integral part of the model and must be controlled carefully to give the best results. Generally, the higher the number of elements in a mesh, the more accurate the solution of the discretized problem. However, there is a value at which the results converge, and further mesh refinement does not increase accuracy. [30]
A Markov process is called a reversible Markov process or reversible Markov chain if there exists a positive stationary distribution π that satisfies the detailed balance equations [13] =, where P ij is the Markov transition probability from state i to state j, i.e. P ij = P(X t = j | X t − 1 = i), and π i and π j are the equilibrium probabilities of being in states i and j, respectively ...
A simple two-point estimation is to compute the slope of a nearby secant line through the points (x, f(x)) and (x + h, f(x + h)). [1] Choosing a small number h , h represents a small change in x , and it can be either positive or negative.
The initial guess will be x 0 = 1 and the function will be f(x) = x 2 − 2 so that f ′ (x) = 2x. Each new iteration of Newton's method will be denoted by x1 . We will check during the computation whether the denominator ( yprime ) becomes too small (smaller than epsilon ), which would be the case if f ′ ( x n ) ≈ 0 , since otherwise a ...
The second extends the domain and uses Discrete Fourier Transform to decorrelate the data, which results in a diagonal precision matrix. The third one requires a metric on X {\displaystyle {\mathcal {X}}} and takes advantage of the so-called screening effect assuming that Λ i , j ≠ 0 {\displaystyle \mathbf {\Lambda } _{i,j}\neq 0} only if d ...