導関数

The instantaneous rate of change describes how a moving object's position changes at a specific moment in time. Mathematically, this is captured by the derivative of a function at a point. The derivative represents the slope of the tangent line to the curve at that location, quantifying how the function's output changes per infinitesimal change in input.

A derivative is defined as the limit of the difference quotient as the interval approaches zero. This limit process captures the instantaneous rate at which a function changes. The derivative provides the slope of the tangent line to the graph at a specific point, enabling precise measurement of how output responds to infinitesimal input changes.

To find the derivative of a square root function, multiply both the numerator and denominator by the conjugate of the numerator. This rationalizing technique eliminates radicals from the numerator, transforming the complex difference quotient into a simpler fractional form that can be evaluated as a limit.

For the square root function, the derivative is 1/(2√x) for all x greater than zero. This expression shows that the rate of change decreases as the input increases. At x=1, the slope is 1/2; at x=4, it is 1/4, demonstrating diminishing sensitivity of output to input changes.

At x=0, the derivative does not exist because the difference quotient fails to approach a finite limit. Graphically, this corresponds to a vertical tangent line at the origin, indicating an abrupt change in direction. This non-differentiable point highlights the importance of smoothness in defining derivatives.

Use the derivative formula 1/(2√x) and substitute the x-coordinate of your point. At x=1, substitute to get 1/(2√1) = 1/2. At x=4, substitute to get 1/(2√4) = 1/4. These values represent the exact slopes of the tangent lines at those locations on the curve.

The derivative function maps each input to its corresponding instantaneous rate of change. For the square root function, the derivative function 1/(2√x) shows how the slope varies across all positive x-values. Understanding the derivative as a function reveals patterns in how sensitivity to input changes evolves throughout the domain.