# How to display very small numbers in Mathematica?

I am trying to evaluate the function: $$f(x) = \cos(x) - \mathrm{e}^{-2.7 x}$$ at $$x = 1.7 \times 10^{-25}$$

and Mathematica keeps returning '0.'

How do I evaluate the expression in a better way?

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Can also use exact numbers.

f[x_] = Cos[x] - E^(-27 x/10);

f[17 10^-26]//N[#,50]&
(*4.5899999999999999999999988020950000000000000000002*10^-25*)


Another way is to increase the floating point digits.

f[x_] = Cos[x] - E^(-2.750 x)

f[1.750 10^-25]
(*4.5899999999999999999999988020950000000000000000002*10^-25*)


These methods pretty much work for any calculation that needs extra accuracy over what machine precision provides.

First convert the expression to trigonometric form:

y = Cos[x] - Exp[-2.7*x] // ExpToTrig


Cos[x] - Cosh[2.7 x] + Sinh[2.7 x]

y /. x -> 1.7*10^-25


4.59*10^-25

This is the exact solution.

Note: Building on the interesting comments on whether the above is the exact solution, it is worth noting that there is an exact relationship between the three numbers: 1.7, 2.7 and 4.59. Actually 1.7x2.7=4.59. That is why 4.59*10^-25 is the exact solution.

Also, the solution approaches 4.59*10^-25 exactly (and symmetrically) from above and below, for example:

y /. x -> 1.69999*10^-25


4.58997*10^-25

y /. x -> 1.70001*10^-25


4.59003*10^-25

• why ExpToTrig ? Jan 20, 2019 at 10:48
• It is a useful tool derived from Euler's formula: en.wikipedia.org/wiki/Euler's_formula Ref: mathworld.wolfram.com/HyperbolicFunctions.html Jan 20, 2019 at 11:18
• Note that this is simply multiplication by the derivative at zero, which is 2.7. when evaluating so close to zero this is a quick way to get the answer yourself. Jan 20, 2019 at 11:19
• It would be quite surprising to me if $f(x)$ on the rational number $x = 1.7 \times 10^-25$ turned out to be a rational number. I suppose it's possible that the transcendental numbers $\cos x$ and $\exp(-2.7 x)$ differ by exactly the rational number $4.59 \times 10^-25$. Mathematica disagrees, though. Possibly I'm not interpreting your meaning of "exact solution" correctly though. I'm assuming "exact" implies we treat $f(x)$ as an exact mathematical function. Jan 21, 2019 at 20:47
• I actually used the term "exact" in the same sense as defined here: "As used in physics, the term "exact" generally refers to a solution that captures the entire physics and mathematics of a problem as opposed to one that is approximate, perturbative, etc. Exact solutions therefore need not be closed-form." Ref: mathworld.wolfram.com/ExactSolution.html Jan 21, 2019 at 21:28

The simplest methods are usually the best. Try this code

N[Cos[x] - Exp[-x 27/10] /. x -> 17*^-26, 15] // InputForm


or a minor variation

With[{x = 17*^-26}, N[Cos[x] - Exp[-x 27/10], 15]] // InputForm


or define a function first

f = Cos[#] - Exp[-# 27/10] &; N[f[17*^-26], 15] // InputForm


All of these return the result

4.58999999999999999999999880209499915.*^-25


You can get more digits by increasing the 15 digits precision.

For example, with 34 digits precision the result returned is

4.589999999999999999999998802095000000000000000000161334.*^-25


All those digits may be spurious since the given numbers $$2.7$$ and $$1.7\times 10^{-25}$$ seem to have only 2 digits of precision. In that case, the answer using 2 digits of precision is $$4.6\times 10^{-25}$$.

Note: In this particular case, given that $$x$$ is small, $$\,|x|<<1,\,$$ then we get $$\cos(x) \approx 1 - \frac{x^2}2, \quad e^{-c\,x} \approx 1 - cx,\quad \cos(x) - e^{-c\,x} \approx c\,x.$$ The simple answer is thus $$2.7 \cdot 1.7\times 10^{-25}$$.

Do a series expansion:

Series[Cos[x] - Exp[-2.7 x], {x, 0, 1}]


(*

SeriesData[x, 0, {2.7}, 1, 2, 1]

*)

Then plug in $$x = 1.7 \times 10^{-25}$$ to get:

$$4.59 \times 10^{-25}$$

This is another machine-precision solution. In @Vixllator's excellent answer, we were lucky that Mathematica put the Sinh term last. I say lucky, because the order was determined by alphabetical order, not by numerical reasons. If the Sinh term is first or second, the sum is 0 (see below†).

The difficulty of computing $$1 + u$$ for small $$u$$ is one reason we have expm1(x) = exp(x)-1 and log1p(x) = log(1+x). These, or their equivalents, are available in Mathematica through the undocumented functions:

InternalExpm1[x]
InternalLogp1[x]


To solve a problem like the OPs, the basic goal is to rewrite a function f[x] for which f[0] == 0 in terms of functions that vanish at x == 0. We can use the identities below to rewrite the OP's function:

Cos[z] == 1 - 2 Sin[z/2]^2
Exp[z] == 1 + InternalExpm1[z]


The constant terms introduced in this process will cancel out since f[0] == 0.

toVanishingFns = # /. {  (* rewrite Cos and Exp *)
Cos[z_] -> 1 - 2 Sin[z/2]^2,
Power[E, z_] :> InternalExpm1[z] + 1
} &;
With[{cleanupRule = {0. -> 0, 1. -> 1, -1. -> -1}},
cleanup = # /. cleanupRule &];  (* cleans up trivial floating point coefficients *)

Block[{x},
ff[x_] = Cos[x] - Exp[-2.7 x] // toVanishingFns // cleanup
]
(*  -InternalExpm1[-2.7 x] - 2 Sin[x/2]^2  *)

ff[1.7*^-25]
(*  4.59*10^-25  *)


†In these orders, the Cosh[] and Cos[] terms, which are 1. exactly, don't cancel out first leaving the Sinh[] term; instead, the Cosh[] and Sinh[] terms first sum to -1. exactly, since the Sinh[] term is less than $MachineEpsilon. Sinh[2.7 x] - Cosh[2.7 x] + Cos[x] // Hold; % /. x -> 1.7*^-25 // ReleaseHold -Cosh[2.7 x] + Sinh[2.7 x] + Cos[x] // Hold; % /. x -> 1.7*^-25 // ReleaseHold (* 0. 0. *)  • If InternalExpm1[] had not been available (e.g. in earlier Mathematica versions), an alternative would have been to use the identity Exp[x] == 1 + 2 Exp[x/2] Sinh[x/2]. Thus, (1 - 2 Sin[z/2]^2) - (1 + 2 Exp[-2.7 z/2] Sinh[-2.7 z/2]) /. z -> 1.7*^-25 yields the same 4.59*10^-25 result. Mar 7, 2019 at 13:29 This answer is a little more complicated than necessary, but I'm documenting my attempt here, nonetheless. I have constructed a function RationalizedN which attempts to rationalize all inexact numbers in the expression evaluation before numerical calculations occur, and then compute the value with requested precision using N: ClearAll@RationalizedN; SetAttributes[RationalizedN, HoldFirst]; RationalizedN[expr_, n_:$MachinePrecision] :=
N[FixedPoint[
ReleaseHold@*ReplaceAll[
x_ :> RuleCondition[
Hold@Evaluate@Rationalize[x, 0],
InexactNumberQ@Unevaluated@x]],
Hold@expr], n];


This essentially provides automation for the changes @BillWatts performed in his answer.

Keeping parts of expressions unevaluated while the expression is being modified is somewhat delicate, and includes undocumented RuleCondition usage.

Now we can define f normally and evaluate it using this function:

ClearAll@f;
f[x_] := Cos[x] - Exp[-2.7 x];

RationalizedN[f[1.7*^-25], 50]


4.5900000000000000217190516840950001027706331277536*10^-25

The minor difference in result with others stems from the fact that inexact numbers such as 1.7*^-n, when represented in binary floating point form in computers, are usually not exactly the same as "intuitive" rational form such as $$17/10^n$$, and not necessarily Rationalized to the expected form.

We can see what's actually going on by replacing Echo in right places of the FixedPoint function argument:

$$\text{Hold}[f(\text{1.7\grave{ }*{}^{\wedge}-25})]$$

$$\rightarrow$$

$$\text{Hold}\left[f\left(\text{Hold}\left[\frac{1}{5882352941176470560401060}\right]\right)\right]$$

$$\rightarrow$$

$$\cos \left(\text{Hold}\left[\frac{1}{5882352941176470560401060}\right]\right)-e^{-2.7 \text{Hold}\left[\frac{1}{5882352941176470560401060}\right]}$$

$$\rightarrow$$

$$\cos \left(\text{Hold}\left[\frac{1}{5882352941176470560401060}\right]\right)-e^{\text{Hold}\left[-\frac{27}{10}\right] \text{Hold}\left[\frac{1}{5882352941176470560401060}\right]}$$

$$\rightarrow$$

$$\cos \left(\frac{1}{5882352941176470560401060}\right)-\frac{1}{e^{27/58823529411764705604010600}}$$

$$\rightarrow$$

4.5900000000000000217190516840950001027706331277536*10^-25

Your constants 1.7 and 2.7 have too little precision for the intermediates generated during the computation to have adequate final precision.

Precision[1.7 * 10^-25]
(* MachinePrecision *)
Precision[1.7]  (* The exponent doesn't matter here. *)
(* MachinePrecision *)
N[MachinePrecision]
(* 15.9546 *)


Your Mathematica instance may have slightly different value of MachinePrecision.

First, let's get the correct answer so we can compare with it. We do this by eliminating floating point. (That is, we switch our number representation from one that implicitly represents intervals to one that represents exact numbers.)

f[x_] := Cos[x] - Exp[-27/10 x]
f[17/10 *10^-25]
(* -E^(-459/1000000000000000000000000000) + Cos[17/100000000000000000000000000]  *)


By considering their power series, we expect both of these terms to have decimal representations which are runs of 0s or 9s separating small islands of other digits. We expect the runs in the exponential to be a little shorter than the denominators. In the cosine, we expect the first run to be about twice as long as the denominator. Let's see.

N[-(1/E^(459/1000000000000000000000000000)), 100]
N[Cos[17/100000000000000000000000000], 100]
(*  -0.99999999999999999999999954100000000000000000000010534049999999999999999998388290350000000000000000185  *)
(*  0.99999999999999999999999999999999999999999999999998555000000000000000000000000000000000000000000000003  *)


So those meet expectations. Then we can do the subtraction, getting catastrophic cancellation.

N[f[17/10*10^-25], 100]
(*  4.589999999999999999999998802095000000000000000000161170964999999999999999981853635932916666666666668*10^-25  *)


This catastrophic cancellation of the leading 24 digits is our problem. Since 24 is greater than MachinePrecision, when Mathematica does the subtraction, the Machine Precision leading digits cancel, leaving 0.,a floating point number representing the interval $$\left[ \frac{-1}{2} * 10^{\text{MachinePrecision}}, \frac{1}{2} * 10^{\text{MachinePrecision}} \right]$$ (possibly excluding either or both endpoints, depending on implementation details of floating point representations of intervals straddling zero). The true answer is in that interval, so the printed result is accurate.

Now we know that we should get $$4.589\dots \times 10^{-25}$$. Let's see what we can do to make that happen.

• We can replace the floating point numbers in the definition and the argument to the function.

Clear[f];
f[x_] := Cos[x] - E^(-27/10 x)
f[17/10*10^-25]
N[f[17/10*10^-25]]
N[f[17/10*10^-25], 2]
N[f[17/10*10^-25], 24]
N[f[17/10*10^-25], 25]

(*  -(1/E^(459/1000000000000000000000000000)) +  Cos[17/100000000000000000000000000]  *)
(*  0.  *)
(*  4.6*10^-25  *)
(*  4.59000000000000000000000*10^-25  *)
(*  4.589999999999999999999999*10^-25  *)


Here, we see N experience catastrophic cancellation when we allow MachinePrecision for intermediates but do not specify a precision goal for the result. When we explicitly set a precision goal for the result, N detects that the result is not zero and gives us the requested precision. If our requested precision doesn't reach to the next island, then we get the rounded result "$$4.590\dots \times 10^{-25}$$".

• If we set the precision on the constant in f, there is no improvement.

Clear[f];
f[x_] := Cos[x] - E^(-2.7100 x)
f[1.7*10^-25]

(*  0.  *)

• If we set the precision on the argument, there is no improvement.

Clear[f];
f[x_] := Cos[x] - E^(-2.7 x)
f[1.7100*10^-25]

(*  0.  *)

• If we set the precision of both,

Clear[f];
f[x_] := Cos[x] - E^(-2.724 x)
f[1.724*10^-25]
Clear[f];
f[x_] := Cos[x] - E^(-2.725 x)
f[1.725*10^-25]

(* 4.59000000000000000000000*10^-25 *)
(* 4.589999999999999999999999*10^-25 *)


we get precision limited by our specifications.

So maybe we wonder: Is there something I can do that leaves the definition of f unaltered, but allows me to improve the precision of evaluation when the argument produces catastrophic cancellation? No. We know that 100 digits of intermediate precision is sufficient to get z result different from zero.

 Clear[f];
f[x_] := Cos[x] - E^(-2.7 x)
N[f[1.7*10^-25], 100]
N[f[1.7*10^-25], {100, 100}]
N[f[1.7100*10^-25], 100]
N[f[17/10*10^-25], 100]

(*  0.  *)
(*  0.  *)
(*  0.  *)
(*  0.  *)


The precision of 2.7 is too low. We have to improve the quality of the constant in the definition of $$f$$ and then, to preserve those gains, we have to improve the quality of the constant in the argument.

Clear[f];
f[x_] := Cos[x] - E^(-2.724 x)
N[f[1.7*10^-25], 100]
N[f[1.7*10^-25], {100, 100}]
N[f[1.7100*10^-25], 100]
N[f[17/10*10^-25], 100]

(*  0.  *)
(*  0.  *)
(*  4.59000000000000000000000*10^-25  *)
(*  4.59000000000000000000000*10^-25  *)


... And if you don't want it rounded to these trailing zeroes, both have to be precise enough.

Clear[f];
f[x_] := Cos[x] - E^(-2.725 x)
N[f[1.7*10^-25], 100]
N[f[1.7*10^-25], {100, 100}]
N[f[1.7100*10^-25], 100]
N[f[17/10*10^-25], 100]

(* 0. *)
(* 0.
(* 4.589999999999999999999999*10^-25 *)
(* 4.589999999999999999999999*10^-25 *)
`