Table of Contents
What is Gradient Descent? #
Gradient descent is a popular optimization algorithm used in machine learning and numerical optimization to minimize a function by iteratively moving in the direction of the steepest descent of the function. In this article, we’ll explore the concept of gradient descent through numerical examples implemented in JavaScript.
Gradient descent is a firstorder optimization algorithm used to find the local minimum of a function. It involves taking steps proportional to the negative of the gradient (or derivative) of the function at the current point. The goal is to iteratively update the parameters (or variables) of the function to minimize a given cost or loss function.
Gradient descent is part of a set of algorithms called Numerical Methods. An article on implementations of a few other numerical methods in JavaScript is available here: Numerical Analysis in JavaScript for Scientific/Mathematical Computation.
Mathematical Concept #
Given a function f(x), the gradient descent algorithm can be expressed as:
x_{n+1} = x_{n}  α∇ f(x_{n})
where:
 x_{n} is the current value of the parameter.
 α (alpha) is the learning rate (step size), a small positive scalar.
 ∇ f(x_{n}) is the gradient (derivative) of f at x_{n}.
Key Steps of Gradient Descent #
 Initialize Parameters: Start with an initial guess for the parameter x.
 Compute Gradient: Calculate the gradient ▽f(x) of the function at the current parameter value.
 Update Parameters: Update the parameter x in the direction opposite to the gradient scaled by the learning rate α
 Repeat: Continue iterating until convergence or a maximum number of iterations is reached.
JavaScript Example: Minimizing a Quadratic Function #
Let’s consider a simple example of using gradient descent to minimize a quadratic function f(x) = x^{2}.
// Gradient Descent to minimize f(x) = x^2
function gradientDescent(initialX, learningRate, maxIterations, tolerance) {
let x = initialX;
for (let i = 0; i < maxIterations; i++) {
const gradient = 2 * x; // Gradient of f(x) = x^2
if (Math.abs(gradient) < tolerance) {
console.log(`Gradient descent converged at iteration ${i}, x = ${x}`);
break;
}
x = x  learningRate * gradient; // Update x using gradient descent
}
return x;
}
// Parameters
const initialX = 5; // Initial guess for x
const learningRate = 0.1; // Learning rate (step size)
const maxIterations = 100; // Maximum iterations
const tolerance = 0.0001; // Convergence tolerance
// Perform gradient descent
const minimizedX = gradientDescent(initialX, learningRate, maxIterations, tolerance);
console.log(`Minimized x: ${minimizedX}`);
In this implementation:
 We define a
gradientDescent
function that performs gradient descent on the function f(x) = x^{2}.  The
gradient
variable represents the gradient of f(x) at the current value of x.  We update x using the gradient descent update rule x = x  α*gradient.
 The process continues until the gradient magnitude falls below a specified tolerance or the maximum number of iterations is reached.
Running the Example #
To run the above JavaScript code, you can execute it in a Node.js environment or directly in a browser console. Adjust the initial parameters (initialX
, learningRate
, maxIterations
, tolerance
) to observe different behaviors of the gradient descent algorithm.
Gradient Descent for Minimizing a Generic Function (Two Variables) #
Let’s consider a generic function f(x, y) of two variables x and y that we want to minimize using gradient descent.
Gradient Calculation (Numerical Approximation) #
The gradient ∇ f(x, y) = [∂f/∂x, ∂f/∂y] can be approximated using finite differences:
∂f/∂x ≈ (f(x + ϵ, y)  f(x, y))/ϵ
∂f/∂y ≈ (f(x, y + ϵ)  f(x, y))/ϵ
where ϵ is a small perturbation.
JavaScript Implementation #
You can experiment with this code here: JavaScript Notebook for Gradient Descent.
// Gradient Descent to minimize a generic function f(x, y)
function gradientDescent2D(f, initialX, initialY, learningRate, maxIterations, tolerance, epsilon) {
let x = initialX;
let y = initialY;
for (let i = 0; i < maxIterations; i++) {
// Compute gradient using numerical approximation
const gradientX = (f(x + epsilon, y)  f(x, y)) / epsilon;
const gradientY = (f(x, y + epsilon)  f(x, y)) / epsilon;
// Update parameters using gradient descent
x = x  learningRate * gradientX;
y = y  learningRate * gradientY;
// Compute magnitude of gradient
const gradientMagnitude = Math.sqrt(gradientX * 2 + gradientY * 2);
// Check convergence
if (gradientMagnitude < tolerance) {
console.log(`Gradient descent converged at iteration ${i}, (x, y) = (${x}, ${y})`);
break;
}
}
return { x, y };
}
// Define the function f(x, y)  Example: f(x, y) = (x3)^2 + (y5)^2
function exampleFunction(x, y) {
return (x3)*2 + (y5)*2;
}
// Parameters
const initialX = 1; // Initial guess for x
const initialY = 1; // Initial guess for y
const learningRate = 0.1; // Learning rate (step size)
const maxIterations = 100; // Maximum iterations
const tolerance = 0.0001; // Convergence tolerance
const epsilon = 0.0001; // Small perturbation for gradient calculation
// Perform gradient descent
const minimizedParams = gradientDescent2D(exampleFunction, initialX, initialY, learningRate, maxIterations, tolerance, epsilon);
console.log(`Minimized (x, y): (${minimizedParams.x}, ${minimizedParams.y})`);
Explanation #
In this implementation:
 We define a
gradientDescent2D
function that takes a generic functionf(x, y)
as input and performs gradient descent to minimize it.  The
exampleFunction
represents the function f(x, y) that we want to minimize (e.g., f(x, y) = (x3)^2 + (y5)^2 for a simple quadratic function).  Inside the loop, the gradients ∂f/∂x and ∂f/∂y are calculated using finite differences.
 The parameters x and y are updated using the gradient descent update rule.
 Convergence is checked based on the magnitude of the gradient.
 The process continues until convergence or a maximum number of iterations is reached.
Running the Example #
To use this gradient descent algorithm for a specific function f(x, y), replace the exampleFunction
with your own function. Adjust the initial parameters (initialX
, initialY
, learningRate
, maxIterations
, tolerance
, epsilon
) to suit your optimization problem.
This extended gradient descent algorithm allows you to minimize a generic function of two variables f(x, y) using numerical gradient approximation and iterative updates. You can apply this approach to optimize various types of functions in machine learning, numerical optimization, and scientific computing by defining the objective function and appropriate parameters. Experiment with different functions and settings to explore the behavior of gradient descent in multidimensional spaces.
Experiment with Gradient Descent #
Gradient descent is a fundamental optimization technique used extensively in machine learning and numerical optimization. In this article, we’ve explored the concept of gradient descent through a simple numerical example implemented in JavaScript. You can apply similar principles to more complex functions and realworld optimization problems by appropriately defining the objective function and its gradient.
Experiment with different functions, learning rates, and initial values to gain a deeper understanding of how gradient descent works and its sensitivity to different parameters. Further exploration can involve implementing gradient descent for more complex functions and applying it to realworld optimization tasks.
Gradient descent is just one of many optimization algorithms used in machine learning and numerical computation. It forms the basis for more sophisticated optimization techniques used in deep learning and neural network training.
The extended gradient descent algorithm allows you to minimize a generic function of two variables f(x,y) using numerical gradient approximation and iterative updates. You can apply this approach to optimize various types of functions in machine learning, numerical optimization, and scientific computing by defining the objective function and appropriate parameters. Experiment with different functions and settings to explore the behavior of gradient descent in multidimensional spaces.
Exercise Optimizing a Function with Arbitrary Number of Variables #
In this tutorial, we’ve explored how to implement gradient descent to optimize a function of two variables using JavaScript. Now, let’s extend this concept to optimize a function with an arbitrary number of variables as an exercise. The reader should try to implement this procedure in JavaScript starting with a Blank Notebook or you can use this as a starting point: JavaScript Notebook for Gradient Descent.
Objective: #
Implement gradient descent to optimize a generic function f with n variables using JavaScript.
Steps: #

Define the Objective Function _f_
 Implement Gradient Calculation
 Compute the gradient ∇f(x) numerically using finite differences:
 Apply Gradient Descent:
 Use the computed gradient to update the variables f iteratively: x_{i}=x_{i}−α ∂f/∂x</sub>=x_{ for i=1,2,…,n, where α is the learning rate.}
 Convergence Criteria:
 Implement a convergence criterion based on the magnitude of the gradient or the change in the function value.
By completing this exercise, you’ll gain handson experience in implementing gradient descent for optimizing a function with an arbitrary number of variables using JavaScript. Experiment with different objective functions and parameters to deepen your understanding of gradientbased optimization techniques in numerical computation and machine learning. Happy coding!