Problem Analysis #9

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Problem Statement

Consider the 10$$\times$$ 10 real symmetric matrix

A=[20101010101010101010102010101010101010101010201010101010101010101020101010101010101010102010101010101010101010201010101010101010101020101010101010101010102010101010101010101010201010101010101010101020].A = \left[ \begin{array}{cccccccccc} 20 & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 10\\ 10 & 20 & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 10\\ 10 & 10 & 20 & 10 & 10 & 10 & 10 & 10 & 10 & 10\\ 10 & 10 & 10 & 20 & 10 & 10 & 10 & 10 & 10 & 10\\ 10 & 10 & 10 & 10 & 20 & 10 & 10 & 10 & 10 & 10\\ 10 & 10 & 10 & 10 & 10 & 20 & 10 & 10 & 10 & 10\\ 10 & 10 & 10 & 10 & 10 & 10 & 20 & 10 & 10 & 10\\ 10 & 10 & 10 & 10 & 10 & 10 & 10 & 20 & 10 & 10\\ 10 & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 20 & 10\\ 10 & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 20 \end{array} \right].

Calculate

N:=det(A).N := \det(A).
Ground Truth
110000000000
Execution Trace
Reasoning Process

[THOUGHT] The given matrix A is a 10x10 symmetric matrix with diagonal entries 20 and off-diagonal entries 10. This matrix can be expressed as ( A = 10I + 10J ), where ( I ) is the identity matrix and ( J ) is the matrix of all ones. The determinant of such a matrix is given by the formula ( \det(A) = (a + nb) \cdot a^{n-1} ), where ( a = 10 ), ( b = 10 ), and ( n = 10 ). Applying the formula:

[ \det(A) = (10 + 10 \cdot 10) \cdot 10^{10-1} = 110 \cdot 10^9 = 110000000000 ]

To verify, we will construct the matrix and compute its determinant using Python. [/THOUGHT] [PYTHON] import numpy as np

n = 10 A = np.full((n, n), 10) np.fill_diagonal(A, 20) det = np.linalg.det(A) print(round(det)) # Rounding to handle floating-point precision issues [/PYTHON]

Tool Output
[PYTHON OUTPUT] 110000000000 None [/PYTHON OUTPUT]
Reasoning Process

[THOUGHT] The Python code output confirms the determinant of matrix ( A ) is ( 110000000000 ). This matches the analytical formula for matrices of the form ( aI + bJ ), where ( a = 10 ), ( b = 10 ), and ( n = 10 ). The result is consistent and error-free. [/THOUGHT] \boxed{110000000000}

Final Model Output
110000000000

Comparative Analysis (P9) (Average across runs)

Avg Time Taken

EXAONE-4.0.1-32B (high)
1180.7
Deepseek-R1-distill-Qwen-32B (high)
114.5
Gemma-3-27B
91.9
Llama-VARCO-8B-Instruct
68.8
Solar-Pro-2 (31B)(high)
30.2
Gemini-3-Pro-Preview
24.9
Deepseek-V3.2
19.3
HCX-007(high)
16.9
Grok-4.1-fast
14.3
A.X-4.0 (72B)
13.9
GPT-oss-20B (high)
11.5
Claude-Opus-4.5
9.4
Qwen3-30B-A3B-2507
8.5
GPT-5.1 (high)
6.9

Avg Token Usage

EXAONE-4.0.1-32B (high)
25940.7
Llama-VARCO-8B-Instruct
12475.0
Solar-Pro-2 (31B)(high)
6433.3
Gemini-3-Pro-Preview
4874.3
Gemma-3-27B
4846.0
Grok-4.1-fast
4259.3
Deepseek-R1-distill-Qwen-32B (high)
4045.7
A.X-4.0 (72B)
3684.7
Deepseek-V3.2
3590.0
Claude-Opus-4.5
3365.3
GPT-oss-20B (high)
3077.7
Qwen3-30B-A3B-2507
2697.0
GPT-5.1 (high)
2694.7
HCX-007(high)
1782.3