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Differential Calculus

Updated on August 4, 2011

Differential calculus is simply the process of finding the gradient of a curve at any point. That may not sound very exciting, but what is fascinating is how many applications calculus has. Calculus is widely used in physics, astronomy, engineering and economics.

To see a fun example of how calculus is used in astronomy, by using calculus to work out the amount of fuel to place on board a rocket, read this article:

How Calculus is Used in Astronomy

Calculus is used in any problem where something is changing or moving, and you need to find the rate at which that change occurs. It's also useful for finding maximum points, or optimal solutions. To find out how to use differential calculus to calculate the maximum or minimum point of a parabola, please read:

How To Find the Vertex of a Parabola

Principle of Differential Calculus

To find the gradient of a straight line, as every high school student learns, you need to divide the change in y between two points on the line by the change in x between the two points.

∆(y) / ∆(x) = gradient

A curve can be approximated by a series of straight lines that connect two points on the curve. Working out the gradient of each straight line gives an approximation of how the gradient of the curve varies. The closer together the two points on the curve are, the more accurate the calculation of the gradient will be.

δ(y) / δ(x) ≈ gradient at (x,y)

Here we have used a small delta “δ”, instead of a capital delta “∆”, to indicate that the changes in x and y are very small.

A completely accurate answer to the gradient of the curve is obtained using a tangent of the curve. To find the tangent to the curve, we move the two connected points on the curve closer and closer together until they are both at the same point.

To represent this mathematically, we make our changes in x and y smaller still, like we did when we reduced them from ∆(x) and ∆(y) to δ(x) and δ(y). Now we make them into infinitely small changes, which we represent by dx and dy.

dy/dx = gradient at (x,y)

Graph of y = x^3
Graph of y = x^3

Example: the cubic

As an example, let's calculate the gradient of the curve y = x3 at the point x=2, y=8.

First (poor) approximation:

Approximate the curve as a straight line between x=1.5 and x=2.5

Coordinates of point one: x = 1.5; y = 1.53 = 3.375

Coordinates of point two: x = 2.5; y = 2.53 = 15.625

Gradient ~ (15.625-3.375)/(2.5-1.5) = 12.25

Second (better) approximation:

Approximate the curve as a straight line between x=1.9 and x=2.1

Coordinates of point one: x = 1.9; y = 1.93 = 6.859

Coordinates of point two: x = 2.1; y = 2.13 = 9.261

Gradient ≈ (9.261-6.859)/(2.1-1.9) = 12.01

Using calculus to calculate the gradient (completely accurate)

Let y = x3. Take two points that are infinitely close together to calculate the gradient.

Coordinates of point one: x = x; y = x3.

Coordinates of point two: x = x + dx; y =(x+dx)3 = x3 + 3x2dx + 3xdx2 + dx3

Because “dx” is infinitely small, dx2 ≈ 0 and dx3 ≈ 0

Gradient = (3x2 dx) / dx = 3x2

The gradient of the curve y = x3 at (x,y) is 3x2

For the point x = 2, y = 8, the gradient dy/dx = 3(22) = 12

So our first approximation, 12.25, was quite inaccurate. Our second approximation, using a smaller section of curve, was better, at 12.01.

Quick Reference

y = xn --------------> dy/dx = nxn-1

y = constant ------> dy/dx = 0

y = ex ---------------> dy/dx = ex

y = ln(x) ------------> dy/dx = 1/x

y = sin(x) -----------> dy/dx = cos(x)

y = cos(x) ----------> dy/dx = -sin(x)

Rules of Differentiation

You can repeat the process we just went through to calculate the gradient of any curve, for example:

y = ax2 + bx + c

y = √x

y = ex

By calculating the gradient for all of these curves, you should begin to realise that there are rules for differentiating different types of functions.

Any term in the function that has the form xn differentiates to nxn-1. This works for any value of n, including fractions and negative numbers.

The exponential function, ex, has the special property that it differentiates to itself.

The number e is defined to give this property. It has an approximate value of 2.71828183... Like pi, it is an irrational number: if you write it down as a decimal, it has an infinite number of digits.


Mathematical Methods for Physics and Engineering: A Comprehensive Guide
Mathematical Methods for Physics and Engineering: A Comprehensive Guide

This derivation of calculus is based on that given in Riley, Hobson and Bence's classic textbook. This is a great mathematics textbook for anyone studying physics or engineering at college level.



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    • profile image


      7 years ago

      Tanx for dis piece its quite refreshened my brain

    • profile image

      peter likeman 

      7 years ago

      I always regretted not learning calculus at school and it was a big handicap when i was learning Physics. This is the clearest demonstration I have seen so far but I still don't follow the manoeverings of the squares and cubes. Thank you anyway.

    • topquark profile imageAUTHOR


      8 years ago from UK

      Yes that is annoying. I have thought that I could LaTeX it to pdf and then put it in as an image, but it seems like a long way round. Thanks for the tip, I will have a look at your hubs.


    • Spirit Whisperer profile image

      Xavier Nathan 

      8 years ago from Isle of Man

      I like this. You have explained the concept well so well that anyone with a basic knowledge of High School Mathematics can follow this.

      I think it is difficult to write the mathematics in these hub capsules because they don't have the MathType facility. The way I got around that was to write it in flash and then create a jpeg image that I added to the hub. I have written a few here in HubPages in case you want to see what I mean. I am sure someone here would know what to do and if anyone can I am sure Slarty O'Brian could. Anyway a great job and I will promote it among some of the people I know who will be interested and who will benefit from it. Keep them coming!

      Thank you.


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