Theorem 40.1.1 · Trivial Inequality
For real ,
This means all perfect squares are or more.
If , then is a product of two non-negative numbers. If , then is a product of two negative numbers, which is positive. Equality holds only at .
Squares are never negative, averages beat geometric means, and dot products never beat lengths: three inequalities that find every maximum and minimum on the AMC.
In this section you will be able to use the fact that squares are never negative to find the lowest (or highest) value of a quadratic.
can’t be negative: it’s a sum of squares. But how small can it actually get?
Every inequality in this chapter grows out of one fact: a real number squared is never negative.
To find the lowest point of a quadratic, complete the square. Write it as a square plus a constant. The square is at least , so the constant is the minimum, and it happens when the square is .
The trick also works on bigger expressions. Look for a substitution that turns the expression into “something squared, plus a number”.
For real ,
This means all perfect squares are or more.
If , then is a product of two non-negative numbers. If , then is a product of two negative numbers, which is positive. Equality holds only at .
In a quadratic ,
Complete the square:
The squared term is never negative (Theorem 40.1.1) and is exactly at . Multiplying by keeps it , so ; multiplying by makes it , so .
Simple, yet powerful. This is the core of all inequalities and how more advanced inequalities are derived.
What is the least possible value of for real numbers and ?
What is the least possible value of
where is a real number?
Define
How many integers are there such that ?
In this section you will be able to use AM-GM to make products as big as possible and sums as small as possible, and check when equality holds.
. How big can you make ? Hint: equal parts win.
The arithmetic mean of non-negative numbers is never smaller than their geometric mean. With a fixed sum, the product is biggest when all the numbers are equal. With a fixed product, the sum is smallest when they are equal.
The hard part is setting it up. You need a constant sum (to bound a product) or a constant product (to bound a sum). If the terms don’t cooperate, multiply some of them by weights until they do, then divide back at the end.
Always check the equality case: the bound only counts if equal parts are actually allowed.
For non-negative reals and ,
Basically, this means the average of 2 non-negative numbers (arithmetic mean) is always at least as big as the square root of the product of the 2 numbers (the geometric mean).
Equality holds when .
By the Trivial Inequality, , so , which is . Equality needs , that is .
For , the minimum value of is and occurs when .
Apply AM-GM to and : , so , with equality when , so .
For non-negative reals ,
Equality occurs when (all the variables are equal).
Another way to say this is
This means in general,
Essentially,
Generally, we use AM-GM to maximize products or minimize sums.
For non-negative reals , ,
Weighted AM-GM is very similar to AM-GM. One way to visualize weighted AM-GM is that there are terms which are all equal to . So instead of writing ( times) in our sum we simply write , and instead of writing ( times) in our product we simply write .
We use weighted AM-GM when we are trying to make the sum of all terms a constant by multiplying weights to all (or some) of the terms. Remember to divide by the weights you multiplied at the end.
Let , , and be non-negative integers such that . What is the maximum value of ?
looks like part of an expanded product. Expand . What extra terms appear?
Find the maximum value of .
Think of as the product . Is the sum constant?
What is the maximum value of for real values of ?
Split the fraction: . Let . Write the expression in terms of .
How many ordered pairs of positive real numbers satisfy the equation
Let be the set of rectangular prisms that have volume and surface area . Suppose is the least possible radius of a sphere that can fit any element of inside it. Then can be expressed as , where and are relatively prime positive integers. Find .
There is a smallest positive real number such that there exists a positive real number such that all the roots of the polynomial are real. In fact, for this value of the value of is unique. What is this value of ?
Let be non-negative real numbers such that , and . Let and be positive relatively prime integers such that is the maximum possible value of . Find .
In this section you will be able to use Cauchy-Schwarz (and Titu's Lemma) to link a sum with a sum of squares.
and . How far apart can the largest and smallest possible be?
Think of and as arrows. Their dot product is the product of their lengths times . Since , the squared dot product can’t beat the product of the squared lengths.
That is Cauchy-Schwarz. It turns a plain sum like into a sum of squares: pair with . Equality happens when the arrows point the same way, that is when the ratios match.
Finding an extreme value always takes two steps: prove the bound, then show some numbers reach it.
For reals and ,
This means: (the sum of the products of all and ) the product of the sums of squares of all and all .
Equality holds when the ratio is the same for all .
For every real , by the Trivial Inequality. Expanded, this is the quadratic . A quadratic that is never negative has discriminant : . Equality means some makes every : all ratios equal.
If you ever forget which way the sign faces, just try a small example like , , , and : and .
You generally want to apply Cauchy-Schwarz when you are dealing with sums of squares.
For reals and positive reals ,
Alternately (for non-negative ),
This is a direct consequence of Cauchy-Schwarz.
Apply Cauchy-Schwarz to the vectors and : . Divide by .
Steps to find the maximum/minimum of expressions:
Let , , and be real numbers such that
What is the difference between the maximum and minimum possible values of ?
Treat as fixed and move it across. What is in terms of ?
Suppose that for . Suppose further that . What is the smallest possible value of ?
This problem also appears in the book as 12.6.16.
Let be real numbers such that and . Among all such 100-tuples of numbers, the greatest value that can achieve is , where and are relatively prime positive integers. Find .
In this section you will be able to bound expressions and count solutions of inequality systems.
These practice problems mix inequalities with counting and functions. Each one has three hints if you get stuck.
The other practice problems (40.4.1–40.4.3, 40.4.6, 40.4.8) sit in the lessons they use. Want them all on one page? Open the problem set.
How many ordered triples of integers satisfy the following system of inequalities?
Consider functions that satisfy for all real numbers and . Of all such functions that also satisfy the equation , what is the greatest possible value of
A real-valued function has the property that for all real numbers and ,
Which one of the following cannot be the value of ?
In this section you will be able to mix bounding and equality cases on harder contest problems.
The additional problems. Most are AIME: first find a bound, then build numbers that reach it.
Problems 40.5.2–40.5.5 sit in the lessons they use (AM-GM and Cauchy-Schwarz).
This problem also appears in the book as 40.4.3.
What is the least possible value of for real numbers and ?
Alpha and Beta both took part in a two-day problem-solving competition. At the end of the second day, each had attempted questions worth a total of 500 points. Alpha scored 160 points out of 300 points attempted on the first day, and scored 140 points out of 200 points attempted on the second day. Beta, who did not attempt 300 points on the first day, had a positive integer score on each of the two days, and Beta's daily success rate (points scored divided by points attempted) on each day was less than Alpha's on that day. Alpha's two-day success ratio was . The largest possible two-day success ratio that Beta could achieve is , where and are relatively prime positive integers. What is ?