# Simplification¶

The generic way to do *nontrivial* simplifications of expressions is
calling `simplify()`

function.

```
>>> simplify(sin(x)**2 + cos(x)**2)
1
>>> simplify((x**3 + x**2 - x - 1)/(x**2 + 2*x + 1))
x - 1
>>> simplify(gamma(x)/gamma(x - 2))
(x - 2)⋅(x - 1)
```

There are also more directed simplification functions. These apply
very specific rules to the input expression and are typically able to
make guarantees about the output. For instance, the
`factor()`

function, given a polynomial
with rational coefficients in several variables, is guaranteed to
produce a factorization into irreducible factors.

The `simplify()`

function applies
almost all available in Diofant such specific simplification rules in
some heuristics sequence to produce the simplest result.

Tip

The optional `measure`

keyword argument for
`simplify()`

lets the user specify
the Python function used to determine how “simple” an expression
is. The default is `count_ops()`

, which
returns the total number of operations in the expression.

That is why it is usually slow. But more important pitfail is that
sometimes `simplify()`

doesn’t
“simplify” how you might expect, if, for example, it miss some
transformation or apply it too early or too late. Lets look on an
example

```
>>> simplify(x**2 + 2*x + 1)
2
x + 2⋅x + 1
>>> factor(_)
2
(x + 1)
```

Obviously, the factored form is more “simple”, as it has less arithmetic operations.

The function `simplify()`

is best when
used interactively, when you just want to whittle down an expression
to a simpler form. You may then choose to apply specific functions
once you see what `simplify()`

returns,
to get a more precise result. It is also useful when you have no idea
what form an expression will take, and you need a catchall function to
simplify it.

## Rational Functions¶

`expand()`

is one of the most common
simplification functions in Diofant. Although it has a lot of scopes,
for now, we will consider its function in expanding polynomial
expressions.

```
>>> expand((x + 1)**2)
2
x + 2⋅x + 1
>>> expand((x + 2)*(x - 3))
2
x - x - 6
```

Given a polynomial, `expand()`

will put it
into a canonical form of a sum of monomials with help of more directed
expansion methods, namely
`expand_multinomial()`

and
`expand_mul()`

.

`expand()`

may not sound like a
simplification function. After all, by its very name, it makes
expressions bigger, not smaller. Usually this is the case, but often
an expression will become smaller upon calling
`expand()`

on it due to cancellation.

```
>>> expand((x + 1)*(x - 2) - (x - 1)*x)
-2
```

Function `factor()`

takes a multivariate
polynomial with rational coefficients and factors it into irreducible
factors.

```
>>> factor(x**3 - x**2 + x - 1)
⎛ 2 ⎞
(x - 1)⋅⎝x + 1⎠
>>> factor(x**2*z + 4*x*y*z + 4*y**2*z)
2
z⋅(x + 2⋅y)
```

For polynomials, `factor()`

is the
opposite of `expand()`

.

Note

The input to `factor()`

and
`expand()`

need not be polynomials in
the strict sense. They will intelligently factor or expand any
kind of expression (though, for example, the factors may not be
irreducible if the input is no longer a polynomial over the
rationals).

```
>>> expand((cos(x) + sin(x))**2)
2 2
sin (x) + 2⋅sin(x)⋅cos(x) + cos (x)
>>> factor(_)
2
(sin(x) + cos(x))
```

`collect()`

collects common powers of a
term in an expression.

```
>>> x*y + x - 3 + 2*x**2 - z*x**2 + x**3
3 2 2
x - x ⋅z + 2⋅x + x⋅y + x - 3
>>> collect(_, x)
3 2
x + x ⋅(-z + 2) + x⋅(y + 1) - 3
```

`collect()`

is particularly useful in
conjunction with the `coeff()`

method.

```
>>> _.coeff(x, 2)
-z + 2
```

`cancel()`

will take any rational
function and put it into the standard canonical form, \(p/q\), where \(p\)
and \(q\) are expanded polynomials with no common factors.

```
>>> 1/x + (3*x/2 - 2)/(x - 4)
3⋅x
─── - 2
2 1
─────── + ─
x - 4 x
>>> cancel(_)
2
3⋅x - 2⋅x - 8
──────────────
2
2⋅x - 8⋅x
```

```
>>> expr = (x*y**2 - 2*x*y*z + x*z**2 + y**2 - 2*y*z + z**2)/(x**2 - 1)
>>> expr
2 2 2 2
x⋅y - 2⋅x⋅y⋅z + x⋅z + y - 2⋅y⋅z + z
───────────────────────────────────────
2
x - 1
>>> cancel(_)
2 2
y - 2⋅y⋅z + z
───────────────
x - 1
```

Note

Since `factor()`

will completely
factorize both the numerator and the denominator of an expression,
it can also be used to do the same thing:

```
>>> factor(expr)
2
(y - z)
────────
x - 1
```

However, it’s less efficient if you are only interested in making sure that the expression is in canceled form.

`apart()`

performs a partial fraction
decomposition on a
rational function.

```
>>> (4*x**3 + 21*x**2 + 10*x + 12)/(x**4 + 5*x**3 + 5*x**2 + 4*x)
3 2
4⋅x + 21⋅x + 10⋅x + 12
────────────────────────
4 3 2
x + 5⋅x + 5⋅x + 4⋅x
>>> apart(_)
2⋅x - 1 1 3
────────── - ───── + ─
2 x + 4 x
x + x + 1
```

## Trigonometric Functions¶

To simplify expressions using trigonometric identities, use
`trigsimp()`

function.

```
>>> trigsimp(sin(x)**2 + cos(x)**2)
1
>>> trigsimp(sin(x)**4 - 2*cos(x)**2*sin(x)**2 + cos(x)**4)
cos(4⋅x) 1
──────── + ─
2 2
>>> trigsimp(sin(x)*tan(x)/sec(x))
2
sin (x)
```

It also works with hyperbolic functions.

```
>>> trigsimp(cosh(x)**2 + sinh(x)**2)
cosh(2⋅x)
>>> trigsimp(sinh(x)/tanh(x))
cosh(x)
```

Much like `simplify()`

function,
`trigsimp()`

applies various
trigonometric identities to the input expression, and then uses a
heuristic to return the “best” one.

To expand trigonometric functions, that is, apply the sum or double
angle identities, use `expand_trig()`

function.

```
>>> expand_trig(sin(x + y))
sin(x)⋅cos(y) + sin(y)⋅cos(x)
>>> expand_trig(tan(2*x))
2⋅tan(x)
─────────────
2
- tan (x) + 1
```

## Assumptions¶

By default, Diofant performs all calculations assuming that symbols are commutative with respect to the mutliplication. This assumption makes it easier to treat mathematical problems in full generality.

```
>>> sqrt(x**2)
____
╱ 2
╲╱ x
```

Yet obviously we can simplify above expression if some additional
mathematical properties on `x`

are assumed. This is where
assumptions system come into play.

Assumptions are set on `Symbol`

objects
when they are created. For instance, we can create a symbol that is
assumed to be positive.

```
>>> p = symbols('p', positive=True)
```

And then, certain simplifications will be possible:

```
>>> sqrt(p**2)
p
```

The assumptions system additionally has deductive capabilities. You
might check assumptions on any expression with `is_assumption`

attributes, like `is_positive`

.

```
>>> p.is_positive
True
>>> (1 + p).is_positive
True
>>> (-p).is_positive
False
```

Note

`False`

is returned also if certain assumption doesn’t make sense
for given object.

In a three-valued logic, used by system, `None`

represents the
“unknown” case.

```
>>> (p - 1).is_positive is None
True
```

## Powers and Logarithms¶

`powdenest()`

function applies identity
\((x^a)^b = x^{a b}\), from left to right, if assumptions allow.

```
>>> a, b = symbols('a b', extended_real=True)
>>> powdenest((p**a)**b)
a⋅b
p
```

`powsimp()`

function reduces expression
by combining powers with similar bases and exponent.

```
>>> powsimp(z**x*z**y)
x + y
z
```

Again, as for `powdenest()`

above, for
the identity \(x^a y^a = (x y)^a\), that combine bases, we should be
careful about assumptions.

```
>>> q = symbols('q', positive=True)
>>> powsimp(p**a*q**a)
a
(p⋅q)
```

In general, this identity doesn’t hold. For example, if \(x = y = -1\) and \(a = 1/2\).

`expand_power_exp()`

and
`expand_power_base()`

functions do reverse
of `powsimp()`

.

```
>>> expand_power_exp(x**(y + z))
y z
x ⋅x
>>> expand_power_base((p*q)**a)
a a
p ⋅q
```

Logarithms have similar issues as powers. There are two main identities

- \(\log{(xy)} = \log{(x)} + \log{(y)}\)
- \(\log{(x^n)} = n\log{(x)}\)

Neither identity is true for arbitrary complex \(x\) and \(y\), due to the branch cut in the complex plane for the complex logarithm.

To apply above identities from left to right, use
`expand_log()`

. As for powers, the
identities will not be applied unless they are valid with given set of
assumptions for symbols.

```
>>> expand_log(log(p*q))
log(p) + log(q)
>>> expand_log(log(p/q))
log(p) - log(q)
>>> expand_log(log(p**2))
2⋅log(p)
>>> expand_log(log(p**a))
a⋅log(p)
>>> expand_log(log(x*y))
log(x⋅y)
```

To apply identities from right to left, i.e. do reverse of
`expand_log()`

, use
`logcombine()`

function.

```
>>> logcombine(log(p) + log(q))
log(p⋅q)
>>> logcombine(a*log(p))
⎛ a⎞
log⎝p ⎠
>>> logcombine(a*log(z))
a⋅log(z)
```

## Special Functions¶

Diofant implements dozens of special functions, ranging from functions in combinatorics to mathematical physics.

To expand special functions in terms of some identities, use
`expand_func()`

. For example the gamma
function
`gamma`

can be
expanded as

```
>>> expand_func(gamma(x + 3))
x⋅(x + 1)⋅(x + 2)⋅Γ(x)
```

This method also can help if you would like to rewrite the generalized
hypergeometric function
`hyper`

or the Meijer
G-function `meijerg`

in terms
of more standard functions.

```
>>> expand_func(hyper([1, 1], [2], z))
-log(-z + 1)
─────────────
z
>>> meijerg([[1],[1]], [[1],[]], -z)
╭─╮1, 1 ⎛1 1 │ ⎞
│╶┐ ⎜ │ -z⎟
╰─╯2, 1 ⎝1 │ ⎠
>>> expand_func(_)
z ___
╲╱ ℯ
```

To simplify combinatorial expressions, involving
`factorial`

,
`binomial`

or
`gamma`

— use
`combsimp()`

function.

```
>>> combsimp(factorial(x)/factorial(x - 3))
x⋅(x - 2)⋅(x - 1)
>>> combsimp(binomial(x + 1, y + 1)/binomial(x, y))
x + 1
─────
y + 1
>>> combsimp(gamma(x)*gamma(1 - x))
π
────────
sin(π⋅x)
```