Last updated on July 19th, 2025
We use the derivative of the L2 norm, a powerful tool to understand how the norm changes in response to slight changes in the vector being considered. Derivatives are essential in various real-life applications, such as optimization problems and machine learning. We will now discuss the derivative of the L2 norm in detail.
We now explore the derivative of the L2 norm, which is commonly represented as ||x||₂, where x is a vector. The derivative of the L2 norm is relevant in optimization, especially in gradient descent algorithms. The L2 norm of a vector x is defined as: ||x||₂ = sqrt(x₁² + x₂² + ... + xₙ²) The derivative of the L2 norm with respect to the vector x is given by: d/dx (||x||₂) = x / ||x||₂
The derivative of the L2 norm with respect to a vector x can be denoted as: d/dx (||x||₂) = x / ||x||₂ This formula applies to all vectors x ≠ 0, ensuring the denominator is not zero.
We can derive the derivative of the L2 norm using the chain rule and properties of the square root function. To demonstrate, we consider: ||x||₂ = sqrt(x₁² + x₂² + ... + xₙ²) By applying the chain rule, we have: d/dx (||x||₂) = d/dx (sqrt(x·x)) = (1/2) * (x·x)^(-1/2) * d/dx (x·x) Using the product rule, d/dx (x·x) = 2x: d/dx (||x||₂) = (1/2) * (x·x)^(-1/2) * 2x = x / sqrt(x·x) = x / ||x||₂ Thus, the derivative of the L2 norm is x / ||x||₂.
When a function is differentiated multiple times, the results are known as higher-order derivatives. For the L2 norm, the first derivative gives us the direction of the steepest ascent or descent. Calculating higher-order derivatives of the L2 norm can be complex because further derivatives involve more intricate manipulation of vector calculus and analysis. For the first derivative of the L2 norm, we have: f′(x) = x / ||x||₂ Higher-order derivatives would require progressively more complex differentiation, often beyond basic calculus.
When the vector x is the zero vector, the derivative is undefined because the L2 norm is zero, leading to division by zero.
Students frequently make errors when differentiating the L2 norm, often due to misunderstanding the vector calculus involved. Here are a few common mistakes and how to solve them:
Calculate the derivative of ||x||₂ when x = (3, 4).
For x = (3, 4), the L2 norm is ||x||₂ = sqrt(3² + 4²) = 5. The derivative is given by: d/dx (||x||₂) = x / ||x||₂ = (3, 4) / 5 = (3/5, 4/5) Thus, the derivative is (3/5, 4/5).
We calculate the L2 norm of the vector (3, 4) and then use the derivative formula to find the normalized direction of the vector.
A drone's position is given by the vector p(t) = (t, t²). Find the derivative of its speed, given by the L2 norm of its velocity, at t = 1.
The velocity vector is v(t) = (1, 2t). At t = 1, v(1) = (1, 2). The speed is ||v(t)||₂ = sqrt(1² + (2t)²) = sqrt(1 + 4t²). The derivative of speed with respect to t is: d/dt (||v(t)||₂) = (1 + 4t²)^(-1/2) * (8t) = 8t / sqrt(1 + 4t²) At t = 1, this becomes: 8(1) / sqrt(1 + 4(1)²) = 8 / sqrt(5) Thus, the derivative of the speed at t = 1 is 8 / sqrt(5).
We differentiate the speed of the drone, given by the L2 norm of its velocity, using the chain rule to find how the speed changes at t = 1.
Derive the second derivative of the L2 norm of a vector x = (x₁, x₂).
First, find the first derivative: d/dx (||x||₂) = x / ||x||₂ To find the second derivative, differentiate the first derivative with respect to x again, which involves more complex vector calculus and considerations of directional derivatives and the Hessian matrix.
Higher-order derivatives of the L2 norm are complex and involve advanced vector calculus techniques; they are not typically straightforward.
Prove: d/dx (||x||₂²) = 2x.
Let y = ||x||₂² = x·x. Differentiating, we use: d/dx (x·x) = 2x Thus, d/dx (||x||₂²) = 2x, proving the statement.
By recognizing that ||x||₂² is the dot product of x with itself, we can use basic derivative rules of dot products to arrive at the result.
Solve: d/dx (||x + a||₂) where a is a constant vector.
The derivative is given by: d/dx (||x + a||₂) = (x + a) / ||x + a||₂ This formula uses the same logic as the derivative of the L2 norm for a vector.
We treat x + a as a new vector and apply the standard derivative formula for the L2 norm, adjusting for the constant vector a.
Derivative: A measure of how a function changes as its input changes, showing the rate of change or slope. L2 Norm: A measure of the magnitude of a vector, calculated as the square root of the sum of the squares of its components. Chain Rule: A fundamental rule in calculus used to differentiate the composition of functions. Vector Calculus: A branch of mathematics concerned with differentiation and integration of vector fields. Subgradient: A generalization of the derivative for functions that may not be differentiable everywhere, commonly used in optimization.
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