Homogeneous Function: Definition, Degree & Examples

What Is A Homogeneous Function?

A homogeneous function is a function whose output scales by a fixed power of $t$ when every input is multiplied by the same factor $t$. In symbols, $f$ is homogeneous of degree $n$ when

$$f(tx, ty) = t^{n} f(x, y)$$

holds for all $t$ and for every $(x, y)$ in the domain. The exponent $n$ is called the degree of the function.

Read the rule in words. Stretch or shrink both inputs by the same factor, and the value changes by that factor raised to one fixed power. That fixed power is the entire identity of the function's scaling behaviour, and it is what every test and theorem below comes back to.

The idea is not limited to two variables. A function of $k$ inputs is homogeneous of degree $n$ when $f(t x_1, t x_2, \dots, t x_k) = t^{n} f(x_1, x_2, \dots, x_k)$. Homogeneity is a property a function either has or does not have, which makes it one more way to sort functions, sitting alongside the labels you meet in types of functions and relations and functions, and separate from whether a rule is written as an explicit function or an implicit function.

How Do You Find The Degree Of A Homogeneous Function?

To find the degree, replace $x$ with $tx$ and $y$ with $ty$, then factor out the largest common power of $t$. If what remains is exactly the original function, the function is homogeneous and the exponent of $t$ is the degree. If any $t$ is left stuck inside, the function is not homogeneous.

For a polynomial there is a faster check: add the exponents in each term. If every term has the same total, that total is the degree. This is the same total-degree idea you meet with polynomials and the degree of a polynomial, now read across the whole expression at once.

Example 1: Find the degree of $f(x, y) = x^{3} + 4x^{2}y - 2y^{3}$.

Substitute $tx$ and $ty$:

$$f(tx, ty) = (tx)^{3} + 4(tx)^{2}(ty) - 2(ty)^{3}$$

Simplify each term:

$$f(tx, ty) = t^{3}x^{3} + 4t^{3}x^{2}y - 2t^{3}y^{3}$$

Factor out $t^{3}$:

$$f(tx, ty) = t^{3}\left(x^{3} + 4x^{2}y - 2y^{3}\right) = t^{3} f(x, y)$$

The power that factored out is $3$, and nothing is left behind.

Final answer: $f$ is homogeneous of degree $3$.

What Are Some Examples Of Homogeneous Functions?

Homogeneous functions turn up often once you know the pattern. Each one below is labelled with the degree that factors out under scaling.

• Degree 1: $f(x, y) = x + 3y$, since $f(tx, ty) = t(x + 3y) = t^{1} f(x, y)$.

• Degree 2: $f(x, y) = x^{2} + xy + y^{2}$, since $f(tx, ty) = t^{2}(x^{2} + xy + y^{2})$.

• Degree 3: $f(x, y) = x^{3} + y^{3}$, since $f(tx, ty) = t^{3}(x^{3} + y^{3})$.

• Degree 1 (with a root): $f(x, y) = \sqrt{x^{2} + y^{2}}$, since $\sqrt{t^{2}x^{2} + t^{2}y^{2}} = t\sqrt{x^{2} + y^{2}}$ for $t > 0$.

• Degree 0: $f(x, y) = \dfrac{x}{y}$, since $\dfrac{tx}{ty} = \dfrac{x}{y}$ and no factor of $t$ survives.

Homogeneity is not only a polynomial property. The function $f(x, y) = x^{2}\sin\left(\dfrac{y}{x}\right)$ is homogeneous of degree $2$, because the ratio $\dfrac{y}{x}$ is untouched by scaling and the $x^{2}$ out front carries the factor $t^{2}$.

Which Functions Are Not Homogeneous?

A function fails the test the moment its pieces scale by different powers of $t$, or one piece refuses to scale at all.

• Mixed degrees: $f(x, y) = x^{2} + y$. Scaling gives $t^{2}x^{2} + ty$, and no single power of $t$ factors out cleanly.

• A constant term: $f(x, y) = x^{2} + y^{2} + 1$. Scaling gives $t^{2}x^{2} + t^{2}y^{2} + 1$, and the $1$ stays fixed while the rest grows.

• A non-scaling piece: $f(x, y) = x + \sin y$. The term $\sin(ty)$ does not reduce to a power of $t$ times $\sin y$.

In each case the reason is the same: the output does not change by one clean power of $t$, so no degree exists.

What Is Euler's Theorem For Homogeneous Functions?

Euler's theorem gives a shortcut worth memorising: for a differentiable function that is homogeneous of degree $n$, the inputs and the partial derivatives combine to rebuild the function itself, scaled by $n$.

$$x\frac{\partial f}{\partial x} + y\frac{\partial f}{\partial y} = n, f(x, y)$$

For a function of $k$ variables the pattern extends term by term, $\sum_{i=1}^{k} x_i \frac{\partial f}{\partial x_i} = n f$. The theorem is named for Leonhard Euler, who developed it while studying the calculus of several variables.

Example 2: Verify Euler's theorem for $f(x, y) = x^{2} + xy + y^{2}$.

This function is homogeneous of degree $2$, so the theorem predicts $x\frac{\partial f}{\partial x} + y\frac{\partial f}{\partial y} = 2f$. First the partial derivatives:

$$\frac{\partial f}{\partial x} = 2x + y, \qquad \frac{\partial f}{\partial y} = x + 2y$$

Now form the left side:

$$x(2x + y) + y(x + 2y) = 2x^{2} + xy + xy + 2y^{2}$$

Collect like terms:

$$= 2x^{2} + 2xy + 2y^{2} = 2\left(x^{2} + xy + y^{2}\right) = 2f(x, y)$$

The left side equals $2f$, and the degree is $2$, so the two sides agree.

Final answer: $x\frac{\partial f}{\partial x} + y\frac{\partial f}{\partial y} = 2f$, matching $n = 2$.

How Do Homogeneous Functions Connect To Differential Equations?

A first-order equation $\dfrac{dy}{dx} = F(x, y)$ is called homogeneous when $F$ is a homogeneous function of degree zero, meaning $F(tx, ty) = F(x, y)$. A degree-zero function depends only on the ratio $\dfrac{y}{x}$, so the whole equation can be rewritten in terms of one new variable.

The standard move is the substitution $y = vx$, which turns the equation into a separable one you can integrate. This is where homogeneous functions do their most familiar work in a first calculus course, and it is a standard topic in senior-secondary and early-university syllabuses, including India's NCERT Class 12 differential-equations chapter and UK A-Level and first-year university courses. Homogeneous differential equations sit one step beyond this article.

Why Do Homogeneous Functions Matter?

Homogeneous functions matter because a single number, the degree, captures how a quantity responds to scaling. That one fact shows up wherever making the inputs bigger has a predictable effect on the output.

• Scaling is built into measurement. Double the side of a square and its area grows by $2^{2}$; double the edge of a cube and its volume grows by $2^{3}$. Area is homogeneous of degree $2$, volume of degree $3$.

• The degree encodes behaviour. A degree-1 function grows in step with its inputs, a degree-0 function ignores overall scale and reads only ratios, and higher degrees grow faster than the inputs themselves.

• It simplifies hard equations. Spotting that a differential equation is built from homogeneous functions lets you swap in $y = vx$ and reduce it to something you can integrate.

So the degree is not a label to memorise and forget. It is a compact prediction about what happens when the whole picture is made larger or smaller.

Who Discovered Homogeneous Functions?

The scaling property was used long before it had a tidy name, but one mathematician's name is attached to its most useful theorem: Leonhard Euler.

Two names shaped the idea we use today:

• Leonhard Euler (1707 to 1783, Switzerland) set the theorem inside the calculus of several variables in the mid-1700s, giving the degree its role as the single number that organises the whole family of scaling functions.

• Augustin-Louis Cauchy (1789 to 1857, France) later gave calculus the rigorous definitions of limit and derivative that Euler's relation quietly relies on.

Where Are Homogeneous Functions Used In The Real World?

The same scaling property drives models in fields that rarely talk to each other. In economics it even carries a name, returns to scale.

• Economics: the Cobb-Douglas production function $Q = A,K^{a} L^{b}$ is homogeneous of degree $a + b$. When $a + b = 1$, doubling both capital and labour doubles output, the case called constant returns to scale.

• Thermodynamics: extensive quantities like energy and entropy are homogeneous of degree $1$ in the size of the system, while intensive quantities like temperature and density are homogeneous of degree $0$.

• Engineering and dimensional analysis: scale models of bridges, wings, and ships work because physical quantities scale as fixed powers of length, the same degree-$n$ rule.

• Geometry of measurement: perimeter, area, and volume are homogeneous of degrees $1$, $2$, and $3$, which is why a giant cannot simply be a scaled-up person and stay standing.

One property, read off as a single number, lets each of these fields predict how a system reacts when it grows.

What Are The Most Common Homogeneous Function Mistakes?

Three errors account for most lost marks on this topic. Each follows directly from a clause of the definition, or from the word "homogeneous" being reused for an unrelated idea.

Mislabelling the degree.

Where it slips in:

A student counts the number of terms, or reads only the highest single exponent, instead of adding the exponents within each term.

Don't do this:

Do not call $x^{2}y$ a degree-2 function. Its total degree is $2 + 1 = 3$.

The correct way:

Add the exponents in every term and confirm they match. For a fraction such as $\dfrac{x^{2} + y^{2}}{x + y}$, subtract instead: numerator degree $2$ minus denominator degree $1$ gives degree $1$.

Testing only one value of $t$.

Where it slips in:

A student plugs in $t = 2$, or worse $t = 1$, gets a match, and declares the function homogeneous.

Don't do this:

Do not trust a single number. Setting $t = 1$ makes $f(tx, ty) = f(x, y)$ for every function that exists, homogeneous or not, so it proves nothing.

The correct way:

Keep $t$ as a symbol and require $f(tx, ty)$ to factor as $t^{n} f(x, y)$ for all $t$. Only a general $t$ confirms the property.

Confusing a homogeneous function with a homogeneous system of equations.

Where it slips in:

The word "homogeneous" is reused across topics, so students carry the wrong meaning from one to the other.

Don't do this:

Do not assume the two are related. A homogeneous system of linear equations is one where every equation equals zero, written $A\mathbf{x} = \mathbf{0}$.

The correct way:

Keep the definitions apart. A homogeneous function is about scaling inputs, $f(tx, ty) = t^{n} f(x, y)$; a homogeneous system is about a right-hand side of zero.

Practice Problems On Homogeneous Functions

Work each one, then check the answer beside it.

1. Show that $f(x, y) = x^{3} - 2xy^{2}$ is homogeneous and give its degree.
   (Answer: degree $3$, since $f(tx, ty) = t^{3}(x^{3} - 2xy^{2})$.)

2. Find the degree of $f(x, y) = \dfrac{x^{4} + y^{4}}{x^{2} + y^{2}}$.
   (Answer: degree $2$, from $4 - 2$.)

3. Is $f(x, y) = x^{2} + y^{2} + 3$ homogeneous?
   (Answer: no, the constant $3$ does not scale.)

4. Find the degree of $f(x, y) = \dfrac{x}{y} + \dfrac{y}{x}$.
   (Answer: degree $0$.)

5. Verify Euler's theorem for $f(x, y) = x^{3} + y^{3}$.
   (Answer: $\frac{\partial f}{\partial x} = 3x^{2}$, $\frac{\partial f}{\partial y} = 3y^{2}$, so $x\frac{\partial f}{\partial x} + y\frac{\partial f}{\partial y} = 3x^{3} + 3y^{3} = 3f$, matching degree $3$.)

6. Is $f(x, y) = x^{2}\ln\left(\dfrac{y}{x}\right)$ homogeneous, and of what degree?
   (Answer: yes, degree $2$, since $f(tx, ty) = t^{2}x^{2}\ln(y/x)$.)

Where Should You Go Next After Homogeneous Functions?

Homogeneity is one lens on functions, and a few natural directions open from here.

1. Homogeneous system of linear equations. Meet the other, unrelated use of the word, and see why a right-hand side of zero changes the whole problem.

2. Types of functions. Place homogeneity next to the other big families and see how functions get sorted by behaviour.

3. Even and odd functions. Another property defined by what a substitution does to the output, close in spirit to the scaling test used here.

If your child is building toward calculus, a live Bhanzu trainer teaches these scaling ideas from the ground up in the Bhanzu algebra program.