Inequality in mathematics is a relation that compares the relative size or order of two expressions. Rather than asserting that two quantities are equal, an inequality indicates that one is strictly less than, strictly greater than, or at least not less/greater than the other. Standard symbols are <, >, and . For example, a < b means a is strictly less than b.

Basic notation and order structures

Elementary inequalities are either strict (a < b, a > b) or non‑strict (a ≤ b, a ≥ b). Non‑strict symbols allow equality. Inequalities can be chained (for example a < b ≤ c) to express an ordered relationship among several terms. The idea extends beyond numbers to ordered sets and partially ordered sets, where some pairs may be incomparable.

Rules for manipulation

Certain algebraic rules govern valid operations on inequalities. They are transitive: if a < b and b < c, then a < c. Adding the same quantity to both sides preserves the direction. Multiplying or dividing both sides by a positive number preserves the direction, while multiplying or dividing by a negative number reverses it. Taking reciprocals reverses the inequality when both sides are positive. Care is required with nonlinear operations: squaring is not monotone on the whole real line, and applying a nonmonotone function can change the relation.

Solving inequalities

Methods depend on the type of expression. Linear inequalities are solved by isolating the variable and tracking sign reversals. Polynomial and rational inequalities commonly use sign charts determined by real roots and undefined points to identify solution intervals. Absolute‑value inequalities are handled by splitting into cases or using equivalent compound inequalities. Systems of inequalities describe intervals in one dimension or regions (feasible sets) in higher dimensions.

Common named inequalities

  • Triangle inequality: |x + y| ≤ |x| + |y|, fundamental in metric and normed spaces.
  • Arithmetic mean–geometric mean (AM–GM): for nonnegative numbers, the arithmetic mean is at least the geometric mean.
  • Cauchy–Schwarz: bounds inner products in vector spaces and underlies many estimates.
  • Hölder and Minkowski: generalize Cauchy–Schwarz and triangle inequality to Lp spaces.
  • Markov, Chebyshev and Jensen: useful in probability, statistics and convex analysis.

Applications and significance

Inequalities are indispensable across mathematics and its applications. In analysis they provide bounds that control limits, integrals and series; in optimization they express constraints and objective bounds; in probability they quantify tail behavior and expectations; in numerical analysis they yield error estimates and stability results. Combinatorics and number theory also rely on inequalities to give counting or divisibility bounds.

Practical tips and cautions

When manipulating inequalities, carefully track operations that are not monotone (such as multiplication by expressions of unknown sign, taking square roots, or applying nonmonotone functions). Verify endpoint inclusion for non‑strict inequalities and test sample points when using sign charts. In higher dimensions, geometric intuition about feasible regions often helps guide algebraic work.

Further reading

For proofs, systematic treatments and a wide collection of classical inequalities, consult standard texts on real analysis, convexity and inequality theory. For introductions and references see further reading, which collects textbooks and survey material.