In this post, you will learn the concepts of local and global minima with illustrative pictures and examples. Optimization problems are one of the key types of data analytics problems. Prescriptive analytics are mostly optimisation problems. Other types of data analytics problems includes descriptive analytics (what has happened?) and predictive analytics  (what can happen?). Predictive analytics primarily makes use of machine learning (ML) algorithms. ML algorithms are based on optimising (minimising) the cost or loss function. In order to become very good at finding solutions to optimisation problems (relating to minimising functions) including machine learning based problems, one must get a good understanding of the concepts of Local minima / global minima and local maxima / global maxima. In this post, you will learning about local minima and global minima.

## Local Minima and Global Minima

The point at which a function takes the minimum value is called as global minima. However, when the goal is to minimize the function and solved using optimization algorithms such as gradient descent, it may so happen that function may appear to have minimum value at different points. Those several points which appear to be minima but is not the point where the function actually takes the minimum value is called as local minima. Take a look at the following picture to understand the concept of local minima and global minima.

When you draw the function in 3-dimensional space, this is how the local minima and global minima will look like:

In order to find whether a point is a local minima or global minima, one would need to find all possible minima of the function.

Here is an animation which can help you understand the difference between local and global minima in a better manner.

Pay attention to some of the following in the above animation:

• The gradient at different points is found out.
• If the gradient value is positive at a point, it will be required to move to left or reduce the weight.
• If the gradient value is negative at a point, it will be required to increment the value of weight.
• The above two steps are done until the minima is reached.
• The minima could either be local minima or the global minima. There are different techniques which can be used to find local vs global minima. These techniques will be discussed in the future posts.

### How to find minima or maxima of a function?

In order to find the minima or maxima of the function, one needs to determine first-order derivative of that function and equate the same to zero (0). This can give points where you may end up finding both, one or more minima and one or more maxima. Mathematically, this can be represented as the following. Solve the below function and you would get the value of x where the function is zero.

$$\frac{df(x)}{dx} = 0$$

Substitute the value of x in the function and find the value where the function has either minimum values or maximum values. In order to find whether the point is local / global minima or maxima, take the second-order derivative and determine whether the value is positive or negative. If the second order derivative value is positive, the point is local or global minima and if the second order derivative value is negative, the point is local or global maxima. The diagram below represents the same.

You may want to check out my post on gradient descent to understand the importance of finding minima of the function in context of machine learning.

## Conclusions

Here is the summary of what you learned about local minima and global minima of the function:

• A function can have multiple minima and maxima.
• The point where function takes the minimum value is called as global minima. Other points will be called as local minima. At all the minima points, the first order derivative will be zero and related value can be found where the local or global minima occurred.
• Similarly, the point where function takes the maximum value is called as global maxima. Other points will be called as local maxima.
• Local minima and global minima becomes important for machine learning loss or cost function.
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