Calculus • Numerical Integration Flagship

Riemann Sum Calculator

Approximate definite integrals using Left Endpoint ($L_n$), Right Endpoint ($R_n$), Midpoint ($M_n$), Trapezoidal ($T_n$), and Simpson's ($S_n$) numerical integration with interactive rectangle area diagrams.

|
Last Updated: September 2026
|
Verified Mathematical Solution
Target Integrand f(x)
Subinterval Resolution: n = 6 rectangles
Preset Demonstrations:
Approximate Area (L₆) Error: 25.0%
Riemann Sum Approximation
2.0000
Exact Analytical Integral = 2.6667 (8/3)
Δx (Width) 0.3333
Intervals (n) 6
Abs Error 0.6667
Accuracy 75.0%

Area Approximation Partition Plot

∑ f(x*) Δx
f(x) Curve
Approximation Sum

Step-by-Step Riemann Sum Partition Summation Formula: Δx = (b − a) / n

Direct Answer & Overview
Verified Educational Guide

How to Calculate a Riemann Sum

To calculate a Riemann sum for a function f(x) on interval [a, b] with n subintervals, first find the step width Δx = (b - a) / n. For a Left sum, evaluate f(x) at x₀, x₁, ..., x_{n-1} and multiply the sum by Δx. For a Right sum, evaluate at x₁, x₂, ..., x_n. For a Midpoint sum, evaluate at interval centers (x_{i-1} + x_i)/2. For the Trapezoidal rule, average the left and right endpoints with doubled interior terms: T_n = (Δx/2) [f(x₀) + 2f(x₁) + ... + f(x_n)].

Primary Mathematical Formula Standard Mathematical Model
Standard Equation
ƒ(x)
Q.E.D.
Δx = (b - a)/n | L_n = Δx ∑_{i=0}^{n-1} f(x_i) | R_n = Δx ∑_{i=1}^{n} f(x_i) | ∫_a^b f(x)dx = lim_{n→∞} ∑ f(x_i*)Δx
Evaluated with exact mathematical formulation • Rigorously verified
Exact Formula
Input Parameters
Required
1
Integrand Function: Preset algebraic, exponential, or trigonometric function f(x)
2
Interval Bounds: Lower limit a and upper limit b
3
Subinterval Count (n): Number of partitioning rectangles (e.g. n = 4, 6, 10, 20)
Expected Outputs
Calculated
Approximated Area: Exact sum value for the chosen numerical rule
Exact Analytical Integral: Fundamental Theorem comparison ∫_a^b f(x) dx
Absolute & Percentage Error: Deviation between approximation and exact integral
Partition Width (Δx): Step size for each rectangular base
Worked Numerical Example
Instant Verification
Approximate ∫₀² x² dx using Left Riemann sum with n = 4
→ Δx = (2 - 0)/4 = 0.5. Sample points: x = 0, 0.5, 1.0, 1.5. L₄ = 0.5 [0² + 0.5² + 1.0² + 1.5²] = 0.5 [0 + 0.25 + 1.0 + 2.25] = 0.5(3.5)
L₄ = 1.7500 (Exact = 2.6667, Underestimate by 34.38%)

Anatomy of Riemann Sums & The Definite Integral Definition

Named after the German mathematician Bernhard Riemann (1826–1866), a Riemann sum is the rigorous foundational mechanism used to define the definite integral in calculus.

The continuous region bounded by $y = f(x)$, the $x$-axis, and vertical lines $x = a$ and $x = b$ is sliced into $n$ vertical strips of width $\Delta x = \frac{b - a}{n}$. Each strip's area is approximated by a geometric shape whose height is sampled at a point $x_i^*$ within the strip:

Step Size (Width)
Δx = (b − a) / n

The uniform base width of each subinterval slice.

Sample Point Height
hᵢ = f(xᵢ*)

The vertical height evaluated at left, right, or midpoint coordinates.

Total Partition Sum
Sₙ = ∑ f(xᵢ*) Δx

The summation of all $n$ rectangular or trapezoidal slices.

Left and Right Endpoint Approximation Formulas

Left Riemann Sum (Lₙ)
Lₙ = Δx [ f(x₀) + f(x₁) + ... + f(xₙ₋₁) ]

Evaluates function at the starting left boundary of each slice ($x_0 = a$).

Right Riemann Sum (Rₙ)
Rₙ = Δx [ f(x₁) + f(x₂) + ... + f(xₙ) ]

Evaluates function at the ending right boundary of each slice ($x_n = b$).

The Midpoint Rule (M_n) & Reduced Truncation Error

Rather than choosing an edge, the Midpoint Rule samples $f(x)$ at the exact center of each interval: $\bar{x}_i = \frac{x_{i-1} + x_i}{2}$.

Mₙ = Δx [ f((x₀+x₁)/2) + f((x₁+x₂)/2) + ... + f((xₙ₋₁+xₙ)/2) ]

Because the rectangle cuts through the curve near the middle, the triangular area underestimated on one side roughly cancels the overestimated area on the other side, achieving $O(1/n^2)$ convergence.

The Trapezoidal Rule (T_n) for Linear Segment Interpolation

The Trapezoidal Rule connects consecutive curve points with straight secant chords:

Tₙ = (Δx / 2) [ f(x₀) + 2f(x₁) + 2f(x₂) + ... + 2f(xₙ₋₁) + f(xₙ) ]

Note that interior vertices appear twice because adjacent trapezoids share inner boundaries, while the outer endpoints ($x_0$ and $x_n$) appear only once.

Simpson's Rule (S_n) for Parabolic Segment Approximations

Simpson's Rule fits second-degree quadratic polynomials through consecutive triplets of points:

Sₙ = (Δx / 3) [ f(x₀) + 4f(x₁) + 2f(x₂) + 4f(x₃) + ... + 4f(xₙ₋₁) + f(xₙ) ]

Pattern of coefficients: $1, 4, 2, 4, 2, \dots, 4, 1$. (Requires an even number of subintervals $n$). It is exact for all cubic polynomials ($\deg \le 3$).

Convergence as n Approaches Infinity (The Fundamental Theorem)

The definition of the definite integral as the limit of a Riemann sum:

∫ₐᵇ f(x) dx = lim{n → ∞} ∑{i=1}ⁿ f(xᵢ*) Δx

Step-by-Step Worked Examples (Polynomial and Trigonometric Integrals)

Trapezoidal Approximation Level: Intermediate

Approximate ∫₀² x² dx using Trapezoidal Rule with n = 4.

1. Δx = (2 − 0)/4 = 0.5.

2. Points: x₀=0, x₁=0.5, x₂=1.0, x₃=1.5, x₄=2.0.

3. f(x) values: f(0)=0, f(0.5)=0.25, f(1)=1, f(1.5)=2.25, f(2)=4.

4. T₄ = (0.5 / 2) [0 + 2(0.25) + 2(1) + 2(2.25) + 4] = 0.25 [0 + 0.5 + 2 + 4.5 + 4] = 0.25(11) = 2.7500.

Comparison: Exact = 8/3 ≈ 2.6667 (Error = +3.125%).

Common Pitfalls & Overestimation vs Underestimation Rules

Pitfall 1: Index Offset Mistakes

In a Left Riemann sum with $n$ rectangles, do not include the upper bound $f(b)$; the evaluation stops at $x_{n-1}$. In a Right sum, start at $x_1$ and stop at $x_n = b$.

Pitfall 2: Concavity and Trapezoid Error

For concave-up curves ($f''(x) > 0$), the Trapezoidal rule always overestimates the true integral because secant lines lie above the curve. For concave-down curves ($f''(x) < 0$), it underestimates.

Fact-Checked & Verified • Computational Accuracy Standards
Updated July 2026 • Editorial Policy
Authored By
Sanjay Samanta

Lead Developer & Founder of Basic Math Tools. Specializes in browser-native computational algorithms and applied mathematics.

Reviewed & Verified By
Academic Review Board

Mathematics & curriculum specialists. Audited against standard algebraic and arithmetic principles.

Found an error or have an improvement suggestion? Report a calculation issue

Frequently Asked Questions

What is a Riemann sum in calculus?
A Riemann sum is an approximation of the area under a curve f(x) over an interval [a, b] by partitioning the region into n subintervals and summing the areas of approximating shapes (rectangles, trapezoids, or parabolas).
What is the difference between Left, Right, and Midpoint Riemann sums?
In a Left sum (L_n), the height of each rectangle is evaluated at the left edge of each subinterval (x_{i-1}). In a Right sum (R_n), the height is evaluated at the right edge (x_i). In a Midpoint sum (M_n), the height is evaluated at the midpoint (x_{i-1} + x_i)/2, which generally yields significantly higher numerical accuracy.
How does the Trapezoidal Rule work?
The Trapezoidal Rule replaces the top edge of each rectangular bar with a straight line segment connecting (x_{i-1}, f(x_{i-1})) and (x_i, f(x_i)), forming trapezoids. Its formula is T_n = (Δx/2) [f(x₀) + 2f(x₁) + 2f(x₂) + ... + 2f(x_{n-1}) + f(x_n)], which is exactly the average of the Left and Right Riemann sums.
What is Simpson's Rule and why is it so accurate?
Simpson's Rule approximates the curve using quadratic parabolas across pairs of subintervals rather than flat lines. Its formula is S_n = (Δx/3) [f(x₀) + 4f(x₁) + 2f(x₂) + 4f(x₃) + ... + f(x_n)] (requiring an even number of intervals n). It has an error proportional to O(1/n⁴), making it dramatically more accurate than rectangular rules.
How does a Riemann sum become an exact definite integral?
Taking the limit of the Riemann sum as the number of rectangles approaches infinity (n ⟶ ∞, so Δx ⟶ 0) defines the exact Riemann definite integral: ∫_a^b f(x) dx = lim_{n ⟶ ∞} ∑_{i=1}^n f(x_i*) Δx.
When is a Left or Right Riemann sum an overestimate or underestimate?
For a strictly increasing function (f'(x) > 0), the Left sum is an underestimate and the Right sum is an overestimate. For a strictly decreasing function (f'(x) < 0), the Left sum is an overestimate and the Right sum is an underestimate.