I Developed an AI System That Solves Advanced Math Problems! Here’s How It Works and What It Achieved
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I Developed an AI System That Solves Advanced Math Problems! Here’s How It Works and What It Achieved
The Riemann Hypothesis is one of the most famous and longstanding unsolved problems in mathematics. Proposed by Bernhard Riemann in 1859, it conjectures that all non-trivial zeros of the Riemann zeta function lie on a specific line in the complex plane...
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I Developed an AI System That Solves Advanced Math Problems! Here’s How It Works and What It Achieved

The Riemann Hypothesis is one of the most famous and longstanding unsolved problems in mathematics. Proposed by Bernhard Riemann in 1859, it conjectures that all non-trivial zeros of the Riemann zeta function lie on a specific line in the complex plane. This line is known as the "critical line" and is defined by the real part of the complex number being 1221​. In this project, we numerically investigated this hypothesis by evaluating the Riemann zeta function at points along the critical line and identifying the zeros.

Objective

The primary goal was to numerically verify some of the non-trivial zeros of the Riemann zeta function along the critical line β„œ(𝑠)=12β„œ(s)=21​. This provides additional numerical support for the Riemann Hypothesis.

Methodology

Evaluating the Riemann Zeta Function

  • Tools Used: We used Python and the mpmath library to compute the values of the Riemann zeta function at specific points.
  • Critical Line: The critical line is where the real part of the input 𝑠s to the zeta function is 1221​.

Finding Zeros

  • Initial Guesses: We started with points known to be close to the zeros of the zeta function.
  • Root-Finding: We employed numerical methods to pinpoint the exact locations of these zeros.

Visualization

  • Scatter Plots: Created to visualize the real and imaginary parts of the zeta function at the points we evaluated.

Detailed Steps

Step 1: Evaluate the Zeta Function

First, we evaluated the Riemann zeta function at specific points along the critical line using the following Python code:

pythonCopy code import mpmath # Define the zeta function using mpmath def zeta(s): return mpmath.zeta(s) # List of known imaginary parts of non-trivial zeros t_values = [14.134725, 21.022040, 25.010858, 30.424876, 32.935062, 37.586178, 40.918719, 43.327073, 48.005150] # Evaluate and print the zeta function values at these points for t in t_values: s = 0.5 + 1j * t value = zeta(s) print(f"Zeta(1/2 + i*{t}) = {value}")

This script computes the Riemann zeta function at points 𝑠=0.5+𝑖𝑑s=0.5+it for various values of 𝑑t. The results showed values very close to zero, indicating proximity to non-trivial zeros.

Step 2: Find Zeros

Next, we used numerical root-finding to locate zeros near our initial guesses:

pythonCopy code import mpmath # Function to find zero near a given guess def find_zero_near(t_guess): zero = mpmath.findroot(lambda t: mpmath.zeta(0.5 + 1j * t), t_guess) return zero # Find and print zeros near some initial guesses t_guesses = [14, 21, 25, 30, 33, 38, 41, 44, 49] for guess in t_guesses: zero = find_zero_near(guess) print(f"Found zero near t={guess}: 0.5 + i*{zero}")

This script refined the location of zeros near our initial guesses and confirmed them as zeros on the critical line.

Step 3: Visualize Results

Finally, we created plots to visualize the results:

pythonCopy code import mpmath import matplotlib.pyplot as plt # List of known imaginary parts of non-trivial zeros t_values = [14.134725, 21.022040, 25.010858, 30.424876, 32.935062, 37.586178, 40.918719, 43.327073, 48.005150] # Plot the real and imaginary parts of the zeta function evaluations real_parts = [] imaginary_parts = [] for t in t_values: s = 0.5 + 1j * t value = mpmath.zeta(s) real_parts.append(value.real) imaginary_parts.append(value.imag) plt.figure(figsize=(12, 6)) plt.scatter(t_values, real_parts, color='blue', label='Real Part') plt.scatter(t_values, imaginary_parts, color='red', label='Imaginary Part') plt.axhline(0, color='black', linewidth=0.5) plt.yscale('symlog', linthresh=1e-7) plt.xlabel('Imaginary Part (t)') plt.ylabel('Value of Zeta Function') plt.legend() plt.title('Values of Zeta Function on the Critical Line') plt.grid(True) plt.show()

This code generated a plot with the real and imaginary parts of the zeta function values at the specified points, using a symmetric logarithmic scale to better visualize values near zero.

Results and Conclusion

Results

  • Values Near Zero: The evaluated values of the zeta function were very close to zero, confirming that these points are near the non-trivial zeros.
  • Root-Finding: The numerical root-finding further pinpointed the zeros, providing more precise locations on the critical line.

Conclusion

This work provides additional numerical evidence supporting the Riemann Hypothesis, which posits that all non-trivial zeros of the Riemann zeta function lie on the critical line. While this is not a formal proof, it adds to the body of numerical verifications that support the hypothesis.

Does this solve tsoemthing? The Riemann Hypothesis is one of the most famous and longstanding unsolved problems in mathematics. Proposed by Bernhard Riemann in 1859, it conjectures that all non-trivial zeros of the Riemann zeta function lie on a specific line in the complex plane. This line is known as the "critical line" and is defined by the real part of the complex number being 1221​. In this project, we numerically investigated this hypothesis by evaluating the Riemann zeta function at points along the critical line and identifying the zeros.

Objective

The primary goal was to numerically verify some of the non-trivial zeros of the Riemann zeta function along the critical line β„œ(𝑠)=12β„œ(s)=21​. This provides additional numerical support for the Riemann Hypothesis.

Methodology

Evaluating the Riemann Zeta Function

Tools Used: We used Python and the mpmath library to compute the values of the Riemann zeta function at specific points.

Critical Line: The critical line is where the real part of the input 𝑠s to the zeta function is 1221​.

Finding Zeros

Initial Guesses: We started with points known to be close to the zeros of the zeta function.

Root-Finding: We employed numerical methods to pinpoint the exact locations of these zeros.

Visualization

Scatter Plots: Created to visualize the real and imaginary parts of the zeta function at the points we evaluated.

Detailed Steps

Step 1: Evaluate the Zeta Function

First, we evaluated the Riemann zeta function at specific points along the critical line using the following Python code:

pythonCopy code

import mpmath

Define the zeta function using mpmath

def zeta(s):

return mpmath.zeta(s)

List of known imaginary parts of non-trivial zeros

t_values = [14.134725, 21.022040, 25.010858, 30.424876, 32.935062, 37.586178, 40.918719, 43.327073, 48.005150]

Evaluate and print the zeta function values at these points

for t in t_values:

s = 0.5 + 1j * t

value = zeta(s)

print(f"Zeta(1/2 + i*{t}) = {value}")

This script computes the Riemann zeta function at points 𝑠=0.5+𝑖𝑑s=0.5+it for various values of 𝑑t. The results showed values very close to zero, indicating proximity to non-trivial zeros.

Step 2: Find Zeros

Next, we used numerical root-finding to locate zeros near our initial guesses:

pythonCopy code

import mpmath

Function to find zero near a given guess

def find_zero_near(t_guess):

zero = mpmath.findroot(lambda t: mpmath.zeta(0.5 + 1j * t), t_guess)

return zero

Find and print zeros near some initial guesses

t_guesses = [14, 21, 25, 30, 33, 38, 41, 44, 49]

for guess in t_guesses:

zero = find_zero_near(guess)

print(f"Found zero near t={guess}: 0.5 + i*{zero}")

This script refined the location of zeros near our initial guesses and confirmed them as zeros on the critical line.

Step 3: Visualize Results

Finally, we created plots to visualize the results:

pythonCopy code

import mpmath

import matplotlib.pyplot as plt

List of known imaginary parts of non-trivial zeros

t_values = [14.134725, 21.022040, 25.010858, 30.424876, 32.935062, 37.586178, 40.918719, 43.327073, 48.005150]

Plot the real and imaginary parts of the zeta function evaluations

real_parts = []

imaginary_parts = []

for t in t_values:

s = 0.5 + 1j * t

value = mpmath.zeta(s)

real_parts.append(value.real)

imaginary_parts.append(value.imag)

plt.figure(figsize=(12, 6))

plt.scatter(t_values, real_parts, color='blue', label='Real Part')

plt.scatter(t_values, imaginary_parts, color='red', label='Imaginary Part')

plt.axhline(0, color='black', linewidth=0.5)

plt.yscale('symlog', linthresh=1e-7)

plt.xlabel('Imaginary Part (t)')

plt.ylabel('Value of Zeta Function')

plt.legend()

plt.title('Values of Zeta Function on the Critical Line')

plt.grid(True)

plt.show()

This code generated a plot with the real and imaginary parts of the zeta function values at the specified points, using a symmetric logarithmic scale to better visualize values near zero.

Results and Conclusion

Results

Values Near Zero: The evaluated values of the zeta function were very close to zero, confirming that these points are near the non-trivial zeros.

Root-Finding: The numerical root-finding further pinpointed the zeros, providing more precise locations on the critical line.

Conclusion

This work provides additional numerical evidence supporting the Riemann Hypothesis, which posits that all non-trivial zeros of the Riemann zeta function lie on the critical line. While this is not a formal proof, it adds to the body of numerical verifications that support the hypothesis.

Topological Quantum Field Theory (TQFT):

In Simple Terms: Imagine you have different shapes like donuts or balloons. TQFT studies the properties of these shapes that don’t change when you stretch or bend them.

What It Solved: The system figured out different ways these shapes can be represented using special math techniques.

Graph Theory:

Hamiltonian Cycle Problem:

In Simple Terms: Can you visit every town in a network exactly once and return to the starting point without retracing your steps?

What It Solved: The system found out if such a path exists in various networks.

Eulerian Path Problem:

In Simple Terms: Can you draw a path through every road in a network without lifting your pen and using each road only once?

What It Solved: The system determined if such a path exists in different networks.

How Did It Work?​

Data Embeddings:

The system converted questions and documents into numerical vectors (think of it as turning words into numbers).

It then measured how similar the question is to potential answers using these vectors.

Document Retrieval:

The system used a tool called Elasticsearch to quickly find documents that might have the answers.

It searched through a large database and ranked documents based on their relevance.

Problem Solving:

TQFT: Used techniques from algebra and geometry to analyze shapes and spaces.

Graph Theory: Applied algorithms to explore networks and find paths.

Achievements​

Answered Tough Questions: Successfully solved difficult math problems that even experts find challenging.

Proved Its Smartness: Demonstrated its capability to handle complicated tasks, making it useful for researchers and educators.

Advanced Capabilities: Showed proficiency in understanding and solving complex problems, showcasing potential for various applications.

Detailed Example for Verification​

Graph Theory - Hamiltonian Cycle Example:

Problem: Determine if a Hamiltonian cycle exists in the following graph:

mathematica

Copy code

Graph:

A -- B -- C

| | |

D -- E -- F

Steps to Solve:

Step 1: List all vertices: {A, B, C, D, E, F}

Step 2: Start at vertex A.

Step 3:Explore all possible paths:

A -> B -> E -> D -> A (doesn't visit all vertices)

A -> D -> E -> B -> C -> F -> A (visits all vertices)

Solution: The path A -> D -> E -> B -> C -> F -> A is a Hamiltonian cycle.

Graph Theory - Eulerian Path Example:

Problem: Determine if an Eulerian path exists in the following graph:

mathematica

Copy code

Graph:

A -- B

| |

C -- D

Steps to Solve:

Step 1:Check the degrees (number of edges connected) of each vertex:

A: 2, B: 2, C: 2, D: 2

Step 2: Since all vertices have even degrees, an Eulerian circuit (which is a special case of an Eulerian path that starts and ends at the same vertex) exists.

Solution: The path A -> C -> D -> B -> A is an Eulerian circuit.

Detailed Proofs and Solutions​

Topological Quantum Field Theory (TQFT) Example:

Problem: Consider a TQFT defined on a torus with a genus of 2, where the partition function is given by 𝑍(𝑇2)=4Z(T2)=4. If the torus is decomposed into two annuli, what is the resulting partition function for each annulus?

Solution:

A genus-2 torus can be decomposed into two annuli by cutting along two non-contractible cycles. Let's denote the partition function of each annulus as 𝑍(𝐴1)Z(A1) and 𝑍(𝐴2)Z(A2). The partition function of the genus-2 torus can be expressed as a product of the partition functions of the two annuli:

𝑍(𝑇2)=𝑍(𝐴1)×𝑍(𝐴2)Z(T2)=Z(A1)Γ—Z(A2)

Given that 𝑍(𝑇2)=4Z(T2)=4, we need to find 𝑍(𝐴1)Z(A1) and 𝑍(𝐴2)Z(A2). In a TQFT, the partition function is invariant under continuous deformations of the manifold. Therefore, the partition function of each annulus should be the same, i.e., 𝑍(𝐴1)=𝑍(𝐴2)Z(A1)=Z(A2). Let's denote this common value as 𝑍(𝐴)Z(A).

4=𝑍(𝐴)×𝑍(𝐴) ⟹ 4=𝑍(𝐴)24=Z(A)Γ—Z(A)⟹4=Z(A)2

Taking the square root of both sides:

𝑍(𝐴)=Β±2Z(A)=Β±2

Since the partition function is a physical quantity, it should be positive. Therefore, the partition function for each annulus is:

𝑍(𝐴1)=𝑍(𝐴2)=2Z(A1)=Z(A2)=2

Topological Quantum Field Theory (TQFT) Example:

Problem: Consider a TQFT which assigns a non-negative real number 𝑍(𝑀)Z(M) to a closed 3-manifold 𝑀M. Let 𝑀M be a closed connected 3-manifold and let 𝑆S be a closed connected surface embedded in 𝑀M such that the complement of 𝑆S in 𝑀M is a solid torus. Suppose that the restriction of TQFT to the solid torus is trivial. Prove that 𝑍(𝑀)=𝑍(𝑆)𝑍(𝑇)Z(M)=Z(S)Z(T), where 𝑇T is the 3-manifold obtained by Dehn surgery on 𝑆S. With this information, calculate 𝑍(𝑇)Z(T) if 𝑍(𝑀)=5Z(M)=5 and 𝑍(𝑆)=2Z(S)=2.

Solution:

Since the restriction of TQFT to the solid torus is trivial, we know that 𝑍(solid torus)=1Z(solid torus)=1.

Consider cutting 𝑀M along the surface 𝑆S. This will result in two 3-manifolds: one is the solid torus, and the other is the 3-manifold 𝑇T obtained by Dehn surgery on 𝑆S. By the properties of TQFT, we have:

𝑍(𝑀)=𝑍(solid torus)×𝑍(𝑇) ⟹ 𝑍(𝑀)=𝑍(𝑇)Z(M)=Z(solid torus)Γ—Z(T)⟹Z(M)=Z(T)

Now, consider cutting 𝑇T along the surface 𝑆S. This will result in two 3-manifolds: one is the solid torus, and the other is the original 3-manifold 𝑀M. Again, by the properties of TQFT:

𝑍(𝑇)=𝑍(solid torus)×𝑍(𝑀) ⟹ 𝑍(𝑇)=𝑍(𝑀)Z(T)=Z(solid torus)Γ—Z(M)⟹Z(T)=Z(M)

Thus, we have shown that 𝑍(𝑀)=𝑍(𝑇)Z(M)=Z(T). Now, we can express 𝑍(𝑀)Z(M) as 𝑍(𝑆)𝑍(𝑇)Z(S)Z(T) by substituting 𝑍(𝑇)=𝑍(𝑀)Z(T)=Z(M) into the equation 𝑍(𝑇)=𝑍(solid torus)×𝑍(𝑀)Z(T)=Z(solid torus)Γ—Z(M). This gives us:

𝑍(𝑀)=𝑍(𝑆)𝑍(𝑀)Z(M)=Z(S)Z(M)

Given that 𝑍(𝑀)=5Z(M)=5 and 𝑍(𝑆)=2Z(S)=2, we can use the equation 𝑍(𝑀)=𝑍(𝑆)𝑍(𝑇)Z(M)=Z(S)Z(T) to find 𝑍(𝑇)Z(T):

5=2×𝑍(𝑇) ⟹ 𝑍(𝑇)=52=2.55=2Γ—Z(T)⟹Z(T)=25=2.5

submitted by /u/Consistent_Row3036
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