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A variable is considered dependent if it depends on an independent variable. Dependent variables are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables. Independent variables, in turn, are not seen as depending on any other variable in the scope of ...
, a vector in , are dependent variables for which no derivatives are present (algebraic variables), t {\displaystyle t} , a scalar (usually time) is an independent variable. F {\displaystyle F} is a vector of n + m {\displaystyle n+m} functions that involve subsets of these n + m + 1 {\displaystyle n+m+1} variables and n {\displaystyle n ...
An ODE problem can be expanded with the auxiliary variables which make the power series method trivial for an equivalent, larger system. Expanding the ODE problem with auxiliary variables produces the same coefficients (since the power series for a function is unique) at the cost of also calculating the coefficients of auxiliary equations.
The second-order autonomous equation = (, ′) is more difficult, but it can be solved [2] by introducing the new variable = and expressing the second derivative of via the chain rule as = = = so that the original equation becomes = (,) which is a first order equation containing no reference to the independent variable . Solving provides as a ...
We also note that the same shifted generating function technique applied to the second-order recurrence for the Fibonacci numbers is the prototypical example of using generating functions to solve recurrence relations in one variable already covered, or at least hinted at, in the subsection on rational functions given above.
An evaluation of the variables is a function from a subset of variables to a particular set of values in the corresponding subset of domains. An evaluation v {\displaystyle v} satisfies a constraint t j , R j {\displaystyle \langle t_{j},R_{j}\rangle } if the values assigned to the variables t j {\displaystyle t_{j}} satisfy the relation R j ...
The design matrix contains data on the independent variables (also called explanatory variables), in a statistical model that is intended to explain observed data on a response variable (often called a dependent variable). The theory relating to such models uses the design matrix as input to some linear algebra : see for example linear regression.
In particular, this is the case if the matrix A is independent of t. In the general case, however, the expression above is no longer the solution of the problem. The approach introduced by Magnus to solve the matrix initial-value problem is to express the solution by means of the exponential of a certain n × n matrix function Ω(t, t 0):