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In calculus, the derivative of any linear combination of functions equals the same linear combination of the derivatives of the functions; [1] this property is known as linearity of differentiation, the rule of linearity, [2] or the superposition rule for differentiation. [3]
In mathematics, the derivative is a fundamental tool that quantifies the sensitivity to change of a function's output with respect to its input. The derivative of a function of a single variable at a chosen input value, when it exists, is the slope of the tangent line to the graph of the function at that point.
A number of properties of the differential follow in a straightforward manner from the corresponding properties of the derivative, partial derivative, and total derivative. These include: [ 11 ] Linearity : For constants a and b and differentiable functions f and g , d ( a f + b g ) = a d f + b d g . {\displaystyle d(af+bg)=a\,df+b\,dg.}
The Fréchet derivative is quite similar to the formula for the derivative found in elementary one-variable calculus, (+) =, and simply moves A to the left hand side. However, the Fréchet derivative A denotes the function t ↦ f ′ ( x ) ⋅ t {\displaystyle t\mapsto f'(x)\cdot t} .
In formal terms, the derivative is a linear operator which takes a function as its input and produces a second function as its output. This is more abstract than many of the processes studied in elementary algebra, where functions usually input a number and output another number.
The highest order of derivation that appears in a (linear) differential equation is the order of the equation. The term b(x), which does not depend on the unknown function and its derivatives, is sometimes called the constant term of the equation (by analogy with algebraic equations), even when this term is a non-constant function.
The linear map h → J(x) ⋅ h is known as the derivative or the differential of f at x. When m = n , the Jacobian matrix is square, so its determinant is a well-defined function of x , known as the Jacobian determinant of f .
The derivative of () at the point = is the slope of the tangent to (, ()). [3] In order to gain an intuition for this, one must first be familiar with finding the slope of a linear equation, written in the form = +. The slope of an equation is its steepness.