Taylor & Maclaurin Series Calculator
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Series Expansion
Enter a function
Calculate Taylor or Maclaurin series expansion with step-by-step solutions.
Convergence Graph
Calculate a series to see the function vs approximation graph.
Python Compiler
What is a Taylor Series?
A Taylor series represents a function as an infinite sum of polynomial terms calculated from the function's derivatives at a single point. It is one of the most powerful tools in calculus โ enabling us to approximate complex functions like sin(x), ex, and ln(x) using simple polynomials.
Common Maclaurin Series
These series are used so frequently in mathematics and physics that they are worth memorizing.
โ Exponential
ex = 1 + x + xยฒ/2! + xยณ/3! + โฆ
R = โ
โ Sine
sin(x) = x โ xยณ/3! + x5/5! โ โฆ
R = โ
โ Cosine
cos(x) = 1 โ xยฒ/2! + x4/4! โ โฆ
R = โ
โ Natural Log
ln(1+x) = x โ xยฒ/2 + xยณ/3 โ โฆ
R = 1
โ Geometric
1/(1โx) = 1 + x + xยฒ + xยณ + โฆ
R = 1
โ Square Root
โ(1+x) = 1 + x/2 โ xยฒ/8 + โฆ
R = 1
Understanding Convergence
Not every Taylor series converges everywhere. The radius of convergence R tells you how far from the center point the series reliably approximates the function.
โ R = โ
Functions like ex, sin(x), and cos(x) converge for all real x. Their series approximation works everywhere.
โ Finite R
Functions like ln(1+x) and 1/(1โx) only converge within a limited interval around the center. Beyond that, the series diverges.
โ Singularities
The radius of convergence equals the distance to the nearest singularity (point where the function is undefined), even in the complex plane.
ln(1+x) and increase terms to 15. Watch how the graph matches well for |x| < 1 but diverges wildly beyond x = 1.
Real-World Applications
Taylor series arenโt just theoretical โ they power real technology and science every day.
Calculator Chips
Your calculator computes sin(x) and cos(x) using polynomial approximations derived from Taylor series. Hardware implements these as fast multiplications and additions.
Physics Approximations
sin(ฮธ) โ ฮธ for small angles simplifies pendulum equations. Many physics formulas are first-order Taylor approximations.
Machine Learning
Gradient descent uses first-order Taylor approximation. Newtonโs method uses second-order. Higher-order optimization uses more terms.