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Snap, [6] or jounce, [2] is the fourth derivative of the position vector with respect to time, or the rate of change of the jerk with respect to time. [4] Equivalently, it is the second derivative of acceleration or the third derivative of velocity, and is defined by any of the following equivalent expressions: = ȷ = = =.
Isaac Newton's notation for differentiation (also called the dot notation, fluxions, or sometimes, crudely, the flyspeck notation [12] for differentiation) places a dot over the dependent variable. That is, if y is a function of t , then the derivative of y with respect to t is
The second derivative test can still be used to analyse critical points by considering the eigenvalues of the Hessian matrix of second partial derivatives of the function at the critical point. If all of the eigenvalues are positive, then the point is a local minimum; if all are negative, it is a local maximum.
The derivatives in the table above are for when the range of the inverse secant is [,] and when the range of the inverse cosecant is [,]. It is common to additionally define an inverse tangent function with two arguments , arctan ( y , x ) {\textstyle \arctan(y,x)} .
The covariant derivative incorporates the 4-gradient plus spacetime curvature effects via the Christoffel symbols The strong equivalence principle can be stated as: [ 4 ] : 184 "Any physical law which can be expressed in tensor notation in SR has exactly the same form in a locally inertial frame of a curved spacetime."
The higher-order derivative test or general derivative test is able to determine whether a function's critical points are maxima, minima, or points of inflection for a wider variety of functions than the second-order derivative test. As shown below, the second-derivative test is mathematically identical to the special case of n = 1 in the ...
The solutions to an exact differential equation are then given by (, ()) = and the problem reduces to finding ψ ( x , y ) {\displaystyle \psi (x,y)} . This can be done by integrating the two expressions M ( x , y ) d x {\displaystyle M(x,y)\,dx} and N ( x , y ) d y {\displaystyle N(x,y)\,dy} and then writing down each term in the resulting ...
The q-derivative is a special case of the Hahn difference, [2] (+) +. The Hahn difference is not only a generalization of the q-derivative but also an extension of the forward difference. Also note that the q-derivative is nothing but a special case of the familiar derivative.