Collatz Conjecture

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Collatz Conjecture

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Asset Durmagambetov^*

,Aniyar Durmagambetova

Asset Durmagambetov^*

,Aniyar Durmagambetova

This version is not peer-reviewed

Submitted:

06 February 2024

Posted:

07 February 2024

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Abstract

This paper presents an analysis of the number of zeros in the binary representation of natural numbers. The primary method of analysis involves the use of the concept of the fractional part of a number, which naturally emerges in the determination of binary representation. This idea is grounded in the fundamental property of the Riemann zeta function, constructed using the fractional part of a number. Understanding that the ratio between the fractional and integer parts of a number, analogous to the Riemann zeta function, reflects the profound laws of numbers becomes the key insight of this work. The findings suggest a new perspective on the interrelation between elementary properties of numbers and more complex mathematical concepts, potentially opening new directions in number theory and analysis.

Keywords:

Subject: Computer Science and Mathematics - Analysis

1. Introduction

We will use the following well-known fact: if, for the members of the Collatz sequence, zeros predominate in their binary representation, then these members will lead to a decrease in the subsequent members according to the Collatz rule. A striking example is when the initial number in the Collatz sequence is equal to

2^{n}

. Let’s write the solution of the equation

n = 2^{x}

in the form

x = {x} + [x]

and note that the smaller x, the more zeros in the corresponding binary representation for n. Developing this idea, we come to the following steps.

Analysis of the binary representation of simple cases of natural numbers.
Creation of a process for decomposing an arbitrary natural number into powers of two.
Analysis of the proximity of the process to binary decomposition at the completion of decomposition at each stage.
Calculation of the number of zeros in the binary decomposition of a natural number.
Estimation of the Collatz sequence members depending on the number of ones in the binary decomposition.

2. Results

This document reveals a comprehensive solution to the Collatz Conjecture, as first proposed in [1]. The Collatz Conjecture, a well-known unsolved problem in mathematics, questions whether iterative application of two basic arithmetic operations can invariably convert any positive integer into 1. It deals with integer sequences generated by the following rule: if a term is even, the subsequent term is half of it; if odd, the next term is the previous term tripled plus one. The conjecture posits that all such sequences culminate in 1, regardless of the initial positive integer. Named after mathematician Lothar Collatz, who introduced the concept in 1937, this conjecture is also known as the 3n + 1 problem, the Ulam conjecture, Kakutani’s problem, the Thwaites conjecture, Hasse’s algorithm, or the Syracuse problem. The sequence is often termed the hailstone sequence due to its fluctuating nature, resembling the movement of hailstones. Paul Erdős and Jeffrey Lagarias have commented on the complexity and mathematical depth of the Collatz Conjecture, highlighting its challenging nature. Consider an operation applied to any positive integer:

Divide it by two if it’s even.
Triple it and add one if it’s odd.

This operation is mathematically defined as:

f (n) = \{\begin{matrix} \frac{n}{2}, & if n \equiv 0 mod 2, \\ 3 n + 1, & if n \equiv 1 mod 2 . \end{matrix}

A sequence is formed by continuously applying this operation, starting with any positive integer, where each step’s result becomes the next input. The Collatz Conjecture asserts that this sequence will always reach 1 Recent substantial advancements in addressing the Collatz problem have been documented in works [2]. Now let’s move on to our research, which we will conduct according to the announced plan. For this, we will start with the following

Theorem 1.

Let

\begin{matrix} M & \in N, \\ [α_{j}] - [α_{j + 1}] & = δ_{j} > 0, \\ ϵ_{1} & < 0.65, \\ | F_{j} (x) | & < | x |, \\ α_{j} & = [α_{j}] + ϵ_{j}, \\ ϵ_{j} & < 1, \\ σ_{j} & = 1 - ϵ_{j} . \end{matrix}

M = \sum_{i = 1}^{j - 1} 2^{[α_{i}]} + 2^{α_{j}}, M = \sum_{i = 1}^{j} 2^{[α_{i}]} + 2^{α_{j + 1}},

(1)

Then for

δ_{j} = 1

σ_{j} = 2^{- 1} σ_{j + 1} (1 - \frac{σ_{j + 1} ln 2}{2}) + F_{j} (\frac{σ_{j + 1}^{3}}{12}),

(2)

and for

δ_{j} > 1

σ_{j} = 2^{- δ_{j}} σ_{j + 1} + 1 - \frac{2^{- δ_{j}} - 2^{- 2 δ_{j} + 1}}{ln 2} - 2^{- 2 δ_{j}} \frac{σ_{j + 1}^{2} ln 2}{4} + 2^{- 2 δ_{j}} R_{j} (\frac{{ln}^{2} 2 σ_{j + 1}^{3}}{8}) .

(3)

Proof.

Consider

\begin{matrix} M - M & = 0 = \sum_{i = 1}^{j} 2^{[α_{i}]} + 2^{α_{j + 1}} - [\sum_{i = 1}^{j - 1} 2^{[α_{i}]} + 2^{α_{j}}] \\ = 2^{[α_{j}]} + 2^{α_{j + 1}} - 2^{α_{j}} \\ 2^{α_{j}} & = 2^{[α_{j}]} + 2^{α_{j + 1}} = 2^{[α_{j}]} + 2^{[α_{j + 1}] - [α_{j}] + [α_{j}] + ϵ_{j + 1}} . \end{matrix}

Next, we move to functional relations between

σ_{j}

and

σ_{j + 1}

\begin{matrix} 2^{ϵ_{j}} & = 2^{- δ_{j} + ϵ_{j + 1}} + 1 \\ \Rightarrow 2^{1 - σ_{j}} & = 2^{- δ_{j} + 1 - σ_{j + 1}} + 1 \\ \Rightarrow ln (2^{1 - σ_{j}}) & = ln 2 - σ_{j} ln 2 = ln (2^{- δ_{j} + 1 - σ_{j + 1}} + 1) . \end{matrix}

Calculating for

δ_{j} = 1

, we get:

\begin{matrix} ln (2^{- δ_{j} + 1 - σ_{j + 1}} + 1) |_{δ_{j} = 1} & = ln (2^{- σ_{j + 1}} + 1) \\ = ln 2 + ln (1 - \frac{σ_{j + 1} ln 2}{2} + \frac{σ_{j + 1}^{2} {ln}^{2} 2}{4} + F_{j} (\frac{σ_{j + 1}^{3}}{12})) . \end{matrix}

Continuing calculations for

δ_{j} > 1

, we get:

\begin{matrix} ln (2^{- δ_{j} + 1 - σ_{j + 1}} + 1) & = ln (1 + 2^{- δ_{j} + 1} - 2^{- δ_{j} + 1} \frac{σ_{j + 1} ln 2}{2} + 2^{- δ_{j} + 1} F_{j} (σ_{j + 1}^{2} + 2^{- δ_{j} + 1})) \\ = 2^{- δ_{j}} - 2^{- 2 δ_{j} + 1} - 2^{- δ_{j}} \frac{σ_{j + 1} ln 2}{2} + 2^{- 2 δ_{j}} F_{j} (σ_{j + 1}^{2}) . \end{matrix}

Thus, we obtain the final formulas. □

Theorem 2.

Let

M = 3^{n} = 2^{[α] + {α}} = \sum_{i = 1}^{n^{*}} γ_{i} 2^{i},

1 - {α} > 0.55, n^{*} = [n \frac{ln (3)}{ln (2)}],

(4)

then

\sum_{γ_{i} = 0} 1 \geq \frac{n^{*}}{2} .

Proof.

Let

3^{n} = 2^{α} \Rightarrow α = \frac{n}{ln (3) / ln (2)} \Rightarrow 3^{n} = 2^{[α] + {α}} .

Using Theorem 1, we create a sequence

ϵ_{i}, m_{i}, ϵ_{1} = {α},

2^{ϵ_{1}} = \sum_{k = 0}^{i - 1} 2^{[α_{k}] - α_{1}} + 2^{α_{i} - α_{1}} .

Suppose the binary decomposition process, according to formula (1), stopped at the j-th step. It then follows that the other terms of the decomposition are zeros, and we immediately reach the validity of the theorem’s statement. Therefore, let’s consider the case where the generation of decomposition according to formula (1) does not stop, and j reaches n. This means all

σ_{j} > 0, j < n

Let’s conduct a more detailed analysis of the number of zeros and ones in our binary representation. We introduce the following notations:

l - the number of zeros in the binary representation.

m - the number of ones in the binary representation.

n - the digit length of the binary decomposition, then

n=l+m.

δ_{j} = 1, α_{j} = 0, β_{j} = {((1 - \frac{ln 2 δ_{j + 1}}{2}) / 2 + F_{j} (\frac{σ_{j + 1}^{2}}{12}))}^{- 1}

δ_{j} > 1, α_{j} = - 2^{δ_{j}} (1 - \frac{2^{- δ_{j}} - 2^{- 2 δ_{j} + 1}}{ln 2} + 2^{- δ_{j}} R_{j} (\frac{{ln}^{2} 2 σ_{j + 1}^{3}}{8} + \frac{2^{- 2 δ_{j} + 1}}{ln 2})), β_{j} = 2^{δ_{j}}

To solve the following equations

σ_{j + 1} = α_{j} + β_{j} σ_{j}

we introduce the notations

λ_{k}

- the number of ones after the appearance of

α_{k} > 0

and until the next appearance of zero in the binary decomposition and

γ_{k} = \prod_{m = k + 1}^{k + 1 + λ_{k}} β_{m}, α_{k + 1} > 0

Let’s conduct a series of transformations to understand the following steps.

σ_{j + 1} = α_{j} + β_{j} γ_{j} σ_{j}

σ_{j + 1} = α_{j} + β_{j} γ_{j} (α_{j - 1} + β_{j - 1} γ_{j - 1})

continuing the transformations we get

σ_{n + 1} = α_{n} + β_{1} γ_{1} \frac{α_{1}}{β_{1} γ_{1}} \prod_{k = 0}^{n - 2} γ_{n - m} β_{n - m} + \sum_{m = 1}^{n - 2} γ_{n - m} β_{n - m} \frac{α_{n - m}}{β_{n - m} γ_{n - m}} \prod_{k = 0}^{m - 1} β_{n - k} γ_{n - k} + σ_{1} \prod_{k = 0}^{n - 1} β_{n - k} γ_{n - k}

σ_{n + 1} = α_{n} + \frac{α_{1}}{β_{1} γ_{1}} + \prod_{k = 0}^{n - 1} β_{n - k} γ_{n - k} + \sum_{m = 1}^{n - 2} \frac{α_{n - m}}{β_{n - m} γ_{n - m}} \prod_{k = 0}^{m} β_{n - k} γ_{n - k} + σ_{1} \prod_{k = 0}^{n - 1} β_{n - k} γ_{n - k}

(5)

Introduce the following notations:

α_{*} = inf_{0 \leq i \leq n} \frac{α_{i}}{β_{i}}

α^{*} = sup_{0 \leq i \leq n} \frac{α_{i}}{β_{i}}

A (m) = \sum_{k = 1, δ_{j} = 1}^{m} {ln}_{2} (β_{j}) + \sum_{k = 1, δ_{j} > 1}^{m} {ln}_{2} (β_{j}) = A_{1} (m) + A_{2} (m)

Note that

δ_{k}, σ_{k}

occur at coordinates

x (δ_{k}), x (σ_{k}), x (δ_{k}) = x (σ_{k})

and by definition of

α_{i}

1 < α_{*} < α^{*} < 1.3

Thus, all possible variants with L zeros will be determined by all possible combinations of

(δ_{1}, δ_{2} . . . . δ_{n})

With corresponding coordinates

(x (δ_{1}), x (δ_{2}) . . . . x (δ_{n}))

m_{*} = \sum_{i = 1, δ_{i} > 1}^{n} δ_{i}

Rewrite formula (5)

\frac{σ_{n + 1}}{\prod_{k = 0}^{n - 1} β_{n - k} γ_{n - k}} = \frac{α_{n}}{\prod_{k = 0}^{n - 1} β_{n - k} γ_{n - k}} + \frac{α_{1}}{β_{1} γ_{1}} + \sum_{m = 1}^{n - 1} \frac{α_{n - m}}{β_{n - m} γ_{n - m}} \frac{1}{\prod_{k = m}^{n - 1} β_{n - k} γ_{n - k}} + σ_{1}

σ_{1} \geq (\frac{σ_{n}}{2^{A (n)}} - \frac{α_{*}}{2^{A (n)}} \sum_{i = 1}^{n - 1} 2^{A (i)}) / 2

To calculate the sum in the last inequality, we use the equalities

2^{k} = 1 + \sum_{i = 0}^{k - 1} 2^{i}, 2^{k} + 2^{l} = 2^{l} (1 + \sum_{i = 0}^{k - l - 1} 2^{i}) = 2^{l} + \sum_{i = 0}^{k - l - 1} 2^{i + l} = 2^{l} + \sum_{i = l}^{k - 1} 2^{i}

It is important to note that here k,l also have their coordinates

x (k), x (l)

and all

i, l < i < k,

have coordinates

x (i)

which are built on a uniform grid Thanks to these simple formulas and corresponding coordinates, we can calculate the sums using integrals.

I = \sum_{i = 1}^{n} 2^{A (i)}

I (γ) = \int_{0}^{n} 2^{γ x} d x = I + R (n), γ = \frac{m_{*} + (ln 2 + ϵ) l}{n} >

where R(n) is the residual term which can be neglected for large n

where

L = n - l < m_{*}

- the set level of the number of zeros.

Calculating, we get the following equalities

I (γ) = \frac{1 - 2^{A (n)}}{γ ln 2}

α^{*} \frac{1 - 2^{- A (n)}}{2 ln 2 (1 + ln 2 + ϵ)} \leq σ_{1}, \frac{d I (γ)}{d γ} < 0 \Rightarrow

Note that the smaller

γ

the greater

I (γ)

, therefore to achieve the set level L is only possible with the corresponding

σ_{1}

, and to achieve the level

L = n / 2

, it is necessary to choose

0.55 = \frac{1.3}{2 ln 2 (1 + ln 2 + ϵ)} < σ_{1}

\Rightarrow L \geq n / 2 . \Rightarrow

The statement of the theorem is correct. □

Theorem 3.

Let

a_{n} = \sum_{i = 0}^{n} γ_{i} 2^{i}, n > 1000, γ_{i} \in {0, 1},

then

\exists j^{*} \in {0, 1}, and a_{4 n - j^{*}} < a_{n} .

Proof.

Introduce operators defined as follows:

P f = \frac{f}{2}, T f = 3 f + 1, Z f = 3 f,

T_{i} \in {P, T}, R_{i} \in {Z, P} .

Consider all possible scenarios of Collatz sequence behavior, which can be written in the following form:

\begin{matrix} a_{n + n} = T_{1} T_{2} \dots T_{n} a_{n}, \end{matrix}

We need to estimate each

2 n

-th term of the Collatz sequence based on the number of applied operators

P, T, Z

during n steps.

\begin{matrix} a_{n + n} = T_{n} T_{n - 1} \dots T_{1} a_{n}, \end{matrix}

Let

a_{n}

have m ones in its binary representation, then we count the number of applications of operator Z using the following formula:

m = \sum_{\begin{matrix} R_{i} = Z, \\ i \leq n \end{matrix}} 1,

and the number of applications of operator P using the following formula:

\sum_{\begin{matrix} R_{i} = P, \\ i \leq n \end{matrix}} 1 = m + n - m = n .

Since each application of Z is accompanied by operator P, and the number of applications of operator P corresponds to the number of zeros in

a_{n}

, which equals

n - m

. According to the rules of Collatz, after n steps we have:

a_{n + n} = \frac{3^{m}}{2^{n}} a_{n} + T_{n} T_{n - 1} \dots T_{1} 1 = \frac{3^{m}}{2^{n}} a_{n} + B_{n},

B_{n} \leq 2^{- n + m} \sum_{j = 1}^{m} \frac{3^{j}}{2^{j}} a_{n} < 2^{- n + m} \cdot 3^{m} / 2^{m} \cdot a_{n} \leq 2^{- 2 n + 1} \cdot 3^{m} \cdot a_{n} .

According to the last formula, we see that the growth of each term of the sequence depends on the number of ones in the binary representation. Next, we will show that a large number of ones at the

2 n

-th step leads to an increase in the number of zeros at the

3 n

-th step for binary representation according to the previous theorems, from which it follows that subsequent terms of the sequence decrease:

\begin{matrix} a_{2 n} = 3^{m} a_{n} \cdot 2^{- n} + B_{n} = 3^{m} + 3^{m} (a_{n} - 2^{n}) + B_{n}, \end{matrix}

Repeating the reasoning of Theorem 2, consider the equation

2^{x} = a_{2 n} = 3^{m} a_{n} \cdot 2^{- n} + B_{n} = 3^{m} + 3^{m} (a_{n} - 2^{n}) \cdot 2^{- n} + B_{n},

x ln 2 = m ln (3) + ln (1 + (a_{n} - 2^{n}) \cdot 2^{- n} + B_{n} \cdot 3^{- m}),

From the last equation, to apply the results of theorem 2, we need

σ_{1} > \frac{1}{2 ln 2}

. To satisfy the last inequality, consider

m_{j} = m - j,

θ = (a_{n} - 2^{n}) \cdot 2^{- n}

{x} = min_{j < 10} \{\frac{(m - j) ln (3)}{ln (2)} + \frac{ln (1 + θ)}{ln 2} + F_{j} (\frac{1}{2^{n} ln 2})\},

Consider

p = (m - j) \frac{ln 3}{ln 2} = (2 k + l) 1.5849625007 \dots,

ϵ = 1.5849625007 \dots - 1.5,

we get

p = (2 k + l) (1.5 + ϵ + \frac{ln (1 + θ)}{ln 2}) = 3 k + (2 k + l) \cdot ϵ + \frac{ln (1 + θ)}{ln 2},

{p} = {1.5 \cdot l + (2 k + l) \cdot ϵ + \frac{ln (1 + θ)}{ln 2}} = {1.5 \cdot l + {(2 k + l) \cdot ϵ + \frac{ln (1 + θ)}{ln 2}}},

Choosing l from even numbers less than 10, if inequalities

0 \leq {(2 k) \cdot ϵ + \frac{ln (1 + θ)}{ln 2}} \leq 0.5,

are true

{p} = {2 k \cdot ϵ + \frac{ln (1 + θ)}{ln 2}} = {2 k \cdot ϵ + \frac{ln (1 + θ)}{ln 2}},

Choosing l from odd numbers less than 10, if inequalities

0.5 < {2 k \cdot ϵ + \frac{ln (1 + θ)}{ln 2}} < 1,

are true

{p} = {2 k \cdot ϵ + \frac{ln (1 + θ)}{ln 2}} = {0.5 + (2 k + l) \cdot ϵ},

Using

ϵ < 0.1,

also satisfy the condition

σ_{1} = 1 - {x} > \frac{1}{2 ln 2} .

\begin{matrix} m^{*} number of non - zero γ_{i}, \end{matrix}

According to theorem 2 we get

\begin{matrix} m^{*} \leq n / 2 + (n - j^{*}) \cdot ln 3 / ln 2 / 2, \end{matrix}

According to our application of Collatz rules, we have an element

a_{4 n - j^{*}}

, and the order of its binary representation is

n_{2} = n + (n - j^{*}) \cdot ln 3 / ln 2 / 2,

After

3 n - j^{*}

steps of applying Collatz rules we have

a_{4 n - j^{*}} = \frac{3^{m^{*}}}{2^{2 n - j^{*}}} a_{2 n} + T_{3 n - j^{*}} T_{3 n - 1 - j^{*}} \dots T_{1} 1 = \frac{3^{m^{*}}}{2^{2 n}} a_{2 n} + B_{3 n},

a_{4 n - j^{*}} = \frac{3^{m^{*}}}{2^{2 n}} a_{2 n} + T_{3 n - j^{*}} T_{3 n - j^{*} - 1} \dots T_{1} 1 = \frac{3^{m^{*}}}{2^{2 n}} (\frac{3^{m}}{2^{n - j^{*}}} a_{n} + B_{n}) + B_{3 n - j^{*}},

a_{4 n - j^{*}} = 3^{m^{*} + m} \cdot 2^{- 3 n - j^{*}} a_{n} + 3^{m^{*}} \cdot 2^{- 2 n - j^{*}} B_{n} + B_{3 n - j^{*}},

a_{4 n - j^{*}} \leq q_{1} \cdot a_{n},

By definition of

m^{*}, l^{*}, B_{n}

we get

q_{1} < 1,

Using

n > 1000

, it follows that

q_{1} < 1 \Rightarrow a_{4 n - j^{*}} < a_{n}

. □

Theorem 4.

Let

a_{n} = \sum_{i = 0}^{n} γ_{i} 2^{i}, n > 1000, γ_{i} \in {0, 1},

then

\exists j^{*} < 0.1 n, and a_{4 n - j^{*}} < a_{n} .

Proof.

Let’s introduce operators defined by the formulas

P f = \frac{f}{2}, T f = 3 f + 1, Z f = 3 f,

T_{i} \in {P, T}, R_{i} \in {Z, P} .

Consider all possible scenarios of the behavior of the Collatz sequence, which can be written in the following form:

\begin{matrix} a_{n + n} = T_{1} T_{2} \dots T_{n} a_{n}, \end{matrix}

It is necessary to calculate an estimate for each

2 n

-th member of the Collatz sequence based on the number of

P, T, Z

operators applied during n steps.

\begin{matrix} a_{n + n} = T_{n} T_{n - 1} \dots T_{1} a_{n}, \end{matrix}

Let

a_{n}

have m units in its binary representation, then calculate the number of applications of the Z operator by the following formula:

m = \sum_{\begin{matrix} R_{i} = Z, \\ i \leq n \end{matrix}} 1,

and calculate the number of applications of the P operator by the following formula:

\sum_{\begin{matrix} R_{i} = P, \\ i \leq n \end{matrix}} 1 = m + n - m = n .

Since each application of Z is accompanied by the P operator, and the number of applications of the P operator corresponds to the number of zeros in

a_{n}

, which is equal to

n - m

. According to the rules of Collatz after n steps, we have:

a_{n + n} = \frac{3^{m}}{2^{n}} a_{n} + T_{n} T_{n - 1} \dots T_{1} 1 = \frac{3^{m}}{2^{n}} a_{n} + B_{n},

B_{n} \leq 2^{- n + m} \sum_{j = 1}^{m} \frac{3^{j}}{2^{j}} a_{n} < 2^{- n + m} \cdot 3^{m} / 2^{m} \cdot a_{n} \leq 2^{- 2 n + 1} \cdot 3^{m} \cdot a_{n} .

According to the last formula, we see that the growth of each member of the sequence depends on the number of units in the binary representation. Next, we will show that a large number of units on the

2 n

-th step leads to an increase in the number of zeros in the

3 n

-th step for the binary representation according to previous theorems, hence the reduction of subsequent members of the sequence:

\begin{matrix} a_{2 n} = 3^{m} a_{n} \cdot 2^{- n} + B_{n} = 3^{m} + 3^{m} (a_{n} - 2^{n}) + B_{n}, \end{matrix}

Repeating the reasoning of Theorem 2, consider the equation

2^{x} = a_{2 n} = 3^{m} a_{n} \cdot 2^{- n} + B_{n} = 3^{m} + 3^{m} (a_{n} - 2^{n}) \cdot 2^{- n} + B_{n},

x ln 2 = m ln (3) + ln (1 + (a_{n} - 2^{n}) \cdot 2^{- n} + B_{n} \cdot 3^{- m}),

From the last equation, in order to apply the results of theorem 2, we need

σ_{1} = 1 - {x} > 0.5

. To fulfill the last inequality, consider

m_{j} = m - j,

θ = (a_{n} - 2^{n}) \cdot 2^{- n}

{x} = min_{j \in {0, 1}} \{\frac{(m - j) ln (3)}{ln (2)} + \frac{ln (1 + θ)}{ln 2} + F_{j} (\frac{1}{2^{n} ln 2})\},

Consider

p = (m - j) \frac{ln 3}{ln 2} = (2 k + l) 1.5849625007 \dots,

ϵ = 1.5849625007 \dots - 1.5,

we get

p = (2 k + l) (1.5 + ϵ + \frac{ln (1 + θ)}{ln 2}) = 3 k + (2 k + l) \cdot ϵ + \frac{ln (1 + θ)}{ln 2},

{p} = {1.5 \cdot l + (2 k + l) \cdot ϵ + \frac{ln (1 + θ)}{ln 2}} = {1.5 \cdot l + {(2 k + l) \cdot ϵ + \frac{ln (1 + θ)}{ln 2}}},

Choosing

l = 0

, if the inequalities

0 \leq {(2 k) \cdot ϵ + \frac{ln (1 + θ)}{ln 2}} \leq 0.5

are true,

{p} = {2 k \cdot ϵ + \frac{ln (1 + θ)}{ln 2}} = {2 k \cdot ϵ + \frac{ln (1 + θ)}{ln 2}},

Choosing

l = 1

, if the inequalities

0.5 < {2 k \cdot ϵ + \frac{ln (1 + θ)}{ln 2}} < 1

are true,

{p} = {2 k \cdot ϵ + \frac{ln (1 + θ)}{ln 2}} = {0.5 + (2 k + l) \cdot ϵ},

Using

ϵ < 0.1,

we also satisfy the condition

σ_{1} = 1 - {x} > 0.51 .

\begin{matrix} m^{*} is the number of non - zero γ_{i}, \end{matrix}

According to theorem 2 we get

\begin{matrix} m^{*} \leq n / 2 + (n - j^{*}) \cdot ln 3 / ln 2 / 2, \end{matrix}

According to our application of the Collatz rules, we have the element

a_{4 n - j^{*}}

, and the order of its binary representation is

n_{2} = n + (n - j^{*}) \cdot ln 3 / ln 2 / 2,

After

3 n - j^{*}

steps of applying the Collatz rules, we have

a_{4 n - j^{*}} = \frac{3^{m^{*}}}{2^{2 n - j^{*}}} a_{2 n} + T_{3 n - j^{*}} T_{3 n - 1 - j^{*}} \dots T_{1} 1 = \frac{3^{m^{*}}}{2^{2 n}} a_{2 n} + B_{3 n},

a_{4 n - j^{*}} = \frac{3^{m^{*}}}{2^{2 n}} a_{2 n} + T_{3 n - j^{*}} T_{3 n - j^{*} - 1} \dots T_{1} 1 = \frac{3^{m^{*}}}{2^{2 n}} (\frac{3^{m}}{2^{n - j^{*}}} a_{n} + B_{n}) + B_{3 n - j^{*}},

a_{4 n - j^{*}} = 3^{m^{*} + m} \cdot 2^{- 3 n - j^{*}} a_{n} + 3^{m^{*}} \cdot 2^{- 2 n - j^{*}} B_{n} + B_{3 n - j^{*}},

a_{4 n - j^{*}} \leq q_{1} \cdot a_{n},

By definition of

m^{*}, l^{*}, B_{n}

we get

q_{1} < 1,

Using

n > 1000

, implies

q_{1} < 1 \Rightarrow a_{4 n - j^{*}} < a_{n}

. □

Theorem 5.

Let

a_{n} = \sum_{i = 0}^{n} γ_{i} 2^{i}, n > 1000, γ_{i} \in {0, 1},

then for

a_{n}

the Collatz conjecture is true.

Proof.

The proof follows from Theorems 1-3. □

Proof. Proof follows from theorem 1-3

6. Conclusions

Our assertion proves that after 3n steps, a sequence with an initial binary length of n arrives at a number strictly smaller than the initial one, from which the solution to the Collatz conjecture follows. This is because by applying this process n times, we are guaranteed to arrive at 1.

References

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