Cauchy-Schwarz Inequality: Simple Definition, Example & Proof

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What is the Cauchy-Schwarz Inequality?

The Cauchy-Schwarz Inequality (also called Cauchy’s Inequality, the Cauchy-Bunyakovsky-Schwarz Inequality and Schwarz’s Inequality) is useful for bounding expected values that are difficult to calculate. It allows you to split E[X1, X2] into an upper bound with two parts, one for each random variable (Mukhopadhyay, 2000, p.149).

The formula is:
Cauchy-Schwarz Inequality

Given that X and Y have finite variances.

What this is basically saying is that for two random variables, X and Y, the expected value of the square of them multiplied together E(XY)2 will always be less than or equal to the expected value of the product of the squares of each. E(X2)E(Y2).

Other Versions

The Cauchy-Schwarz inequality was developed by many people over a long period of time. It made its first appearance in Cauchy’s 1821 work Cours d’analyse de l’Ecole Royal Polytechnique and was further developed by Bunyakovsky (1859) and Schwarz (1888). As well as the different names, it can also be expressed in several different ways. For example, the inequality can be written, equivalently, as:
Cov2(X, Y) ≤ σ2x σ2y.

There is another version of the inequality that replaces the expectation E with time integrals. The formula is the same, with the exception of the expectation/integral replacement:
Cauchy-Schwarz Inequality - alternate version of formula with the expectation/integral replacement


The Cauchy-Schwarz inequality is arguably the inequality with the widest number of applications. As well as probability and statistics, the inequality is used in many other branches of mathematics, including:


Example question: use the Cauchy-Schwarz inequality to find the maximum of x + 2y + 3z,
given that x2 + y2 + z2 = 1.

We know that: (x + 2y + 3x)2 ≤ (12 + 22 32)(x2 + y2 + z2) = 14.

Therefore: x + 2y + 3z ≤ √14.

The equality holds when: x/1 = y/2 = z/3.

We are given that: x2 + y2 + z2 = 1,
x = 1/√14, x = 2/√14, x = 3/√14,


This is one of the simplest proofs to visualize. The following “proof without words” is from Nelson (1994):
Cauchy-Schwarz Inequality - proof without words image

Here’s another proof, this time, in words:

Assume that E[X2]>0 and E[Y2]>0, and:
schwarz inequality

It can be shown that: 2|UV| ≤ U2 + V2.

Therefore: 2|E[UV] | ≤ 2E[|UV|] ≤ E[U2] + E[V2] = 2
(E[UV])2 ≤ (E[|UV|])2 ≤ 1.

This implies that:

You can find a comprehensive list of proofs (12 in all!) for the Cauchy-Schwarz inequality in Win & Wu’s paper (PDF).


Bunyakovsky (1859). Inequalities, in Mémoires de l’Académie impériale des sciences de St. Pétersbourg. Retrieved January 7, 2018 from:
Cauchy, A. (1821). Cours d’analyse de l’École royale polytechnique. Retrieved January 7, 2018 from:
Mukhopadhyay, N. (2000). Probability and Statistical Inference. CRC Press.
Nelsen, R. (1994). Proof without words: Cauchy-Schwarz inequality, Math. Mag., 67, no. 1, p. 20. Retrieved January 7, 2018 from:–Proofs_without_Words.pdf
Schwarz, K. (1888). Uber ein die fl¨achen kleinsten fl¨acheninhalts betreffendes problem der variationsrechnung. Retrieved January 7, 2018 from:
Win, H. & Wu, S. (2000). Various proofs of the Cauchy-Schwarz inequality. Retrieved January 1, 2018 from:

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