The Essential Inequalities
AM–GM: . Cauchy–Schwarz: .
In plain terms. AM–GM says an average beats a geometric mean; Cauchy–Schwarz bounds a "dot product" by the sizes of the two lists. Together they settle most olympiad inequalities.
Example. By AM–GM, ; if this gives .
A sum is largest when both sequences are sorted the same way; "smoothing" nudges variables toward equality while keeping the constraint.
In plain terms. Pair big with big to maximize a sum of products; to find an extreme, gently push the variables together and watch the value improve.
Example. To maximize with fixed, smoothing pushes toward , giving the maximum .
Good-to-Know Inequalities
for positive denominators, with equality when the are all equal.
In plain terms. Any sum of "squares over things" is at least the square of the summed tops over the summed bottoms — the fastest tool for minimizing sums of fractions under a linear constraint.
Example. With : , met at .
For positive reals: , all with equality exactly when the numbers are equal.
In plain terms. Four averages, always in this order: quadratic, arithmetic, geometric, harmonic. Contest problems hand you one end of the chain and ask about the other.
Example. is exactly AM HM in disguise.
Before hunting bounds, ask WHERE equality would hold and whether the constraint permits it — an unattainable equality point means the true extremum sits at a boundary instead.
In plain terms. AM–GM promising means nothing if the balancing point violates the constraint: minimize on and the answer is at the endpoint, not .
Example. Equality in Cauchy–Schwarz needs proportional sequences; if the constraint forbids proportionality, look at the boundary.
Weighted AM–GM and the Equality Case
For positive and positive weights with ,
with equality exactly when . To use it, cut the expression into pieces that are equal to each other at the point where you expect equality, chosen so that their product is a constant.
Take logarithms: the claim is , which is Jensen's inequality for the concave function — a chord of a concave graph lies below the graph, so the logarithm of an average beats the average of the logarithms. Equality in Jensen for a strictly concave function forces all the to coincide. With rational weights the statement is ordinary AM–GM on numbers in which appears times, and that is how it is used in practice: "write as two copies of ". The bookkeeping rule is the equality condition read backwards. AM–GM can only be sharp if all the averaged pieces can be equal at once, so guess the extremal point first, then cut the terms so the pieces agree there and multiply to a constant.
Find the minimum of over . (folklore)
Step 1 — the two-term AM–GM, , is useless: the bound still depends on . A constant bound needs a product of pieces that is constant, so the must be cancelled by two factors of .
Step 2 — split :
Step 3 — equality needs the three pieces equal, , so ; and indeed . The minimum is .
How to find it. The exponent on top and below say how many copies to make: balances two copies of . Calculus agrees — vanishes at — but AM–GM proves the global bound in one line, with no second-derivative or endpoint check.
Positive reals satisfy . Find the maximum of . (folklore)
Step 1 — the product has weights , so write the sum as six pieces: , two copies of , three copies of . They still add up to .
Step 2 — AM–GM on the six pieces:
so .
Step 3 — equality needs ; with that is , and . The maximum is .
How to find it. The exponents are the weights. The maximum of on the plane splits in the ratio — Lagrange multipliers find that point, and the weighted AM–GM split proves it is the maximum.
Let and let be positive reals with . Prove that
(IMO 2012/2)
Step 1 — decide what each factor should be compared with. The constraint only controls the product of the , so the bound for should be a constant times to the first power; then the product of the bounds is a constant.
Step 2 — AM–GM on pieces, with the cut into equal parts:
so .
Step 3 — multiply over . The constants telescope,
and the multiply to , so the left side is at least .
Step 4 — strictness. Equality in Step 2 would need for every , but then when , which the constraint forbids. So some factor is strict, and the inequality is strict.
How to find it. The exponent on is the number of pieces AM–GM should use, and the constraint says each bound must contain exactly once. Those two facts force the split of into equal parts. The equality case then explains the strict sign: the only point where every step is tight violates the constraint.
Positive with : minimize . The tempting proof: and , so the product is at least .
Each step is true, but the bound is never reached: only at , and then is not allowed. A bound whose equality case violates the constraint is a true but weak inequality, not the minimum. Expanding instead, the product is , and with (so ) it equals . Since by AM–GM and decreases on , the minimum is at : the value , at . In a chain of inequalities, write down each step's equality point and check that one admissible point makes them all tight.
Find the minimum of over , and prove that it is the minimum.
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Cut into two halves so that the product of the three pieces is constant:
Equality needs , i.e. , , where the value is . So the minimum is .
Cauchy–Schwarz: Choosing What to Pair
For real : , with equality exactly when and are proportional. Titu (Engel form), for :
Hölder, for positive reals: .
The quadratic is a sum of squares, so it is never negative. Written out it is with , , , and a quadratic that never goes negative has discriminant — that is Cauchy–Schwarz. Equality means has a real root , so for every : proportionality. Titu is Cauchy–Schwarz applied to and . Hölder follows from AM–GM: with and so on, , and summing over gives on the right. The skill is choosing the pairing so that the equality case — proportional vectors — happens where you expect the extremum.
Real numbers satisfy . Find the minimum of . (folklore)
Step 1 — pair with the coefficient vector :
Step 2 — the left side is , so .
Step 3 — equality needs ; substituting, , , so the minimum is attained at . Geometrically is the squared distance to the origin, is the normal of the plane, and the distance from the origin to the plane is .
How to find it. A linear constraint and a sum of squares: the constraint's coefficients are the other vector. The equality case, a point along the normal, is the foot of the perpendicular.
Positive reals satisfy . Prove that
(IMO 1995/2)
Step 1 — the variables sit in the denominators, so substitute , , ; then and
using in the last step.
Step 2 — each term is now a square over a positive quantity, the shape of Titu:
Step 3 — by AM–GM, , so the sum is at least .
Step 4 — both steps are tight at , i.e. , where each term is .
How to find it. A cube in the denominator together with invites the reciprocal substitution. Once the numerators are squares and the denominators are linear, Titu is the tool, and the leftover uses the constraint exactly once.
Prove that for all positive reals ,
(USAMO 2004/5)
Step 1 — the left side is a product of three one-variable factors, the right side the cube of a sum: that is the shape of Hölder, which wants each factor to be a sum of three terms with one cube among them.
Step 2 — compare each factor with :
because and have the same sign for (both negative below , both positive above). So each factor is at least .
Step 3 — put the cubes in different positions and apply Hölder:
Step 4 — equality throughout needs , where both sides equal .
How to find it. Test : both sides are , so every lemma must be tight at . The factor is not a natural Hölder term but is, and their difference factors because both and vanish at .
Positive satisfy ; minimize . Tempting chain: , so , and then .
Both inequalities are true, but the first is tight only when and the second only when . No admissible point makes both tight, so is never attained. Pair the terms with the constraint instead: by Titu,
with equality when , i.e. , — an admissible point, so is the minimum.
Prove that for positive reals ,
(IMO Shortlist 1993)
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Multiply each fraction by its numerator on top and bottom: the left side is , so by Titu it is at least , where is the sum of the four new denominators. Expanding , every product of two different variables appears with total coefficient (for instance comes from and from ), so with . Next, is the sum of the six squares , so and . The bound is therefore at least , with equality at .