Sigma Percentile
JEE Advanced 2018
LEVELJEE Main

Animated Solution for Mathematics - Matrices and Determinants: Let be a matrix of order such that all the entries in are from the set . Then, the maximum possible value of the determinant of is ________.

Enter Numerical Value:

Visualized Solution

The Matrix and its Entries

  • Let be a matrix.
  • Entries .
  • We need to find the maximum possible value of .

Determinant Expansion via Sarrus Rule

  • To find the determinant, we can use the Sarrus Rule.
  • Write the first two columns again next to the matrix.

Positive and Negative Terms

  • are products of main diagonals.
  • are products of anti-diagonals.

The Theoretical Maximum

  • Since , each term is , or .
  • To maximize , we want and .
  • Theoretical maximum .

The Product Trick

  • Let's multiply all 6 terms together.
  • Notice that every matrix element appears exactly twice!

The Perfect Square

  • Since it is a square of real numbers, .

Contradiction for Determinant 6

  • If , then and .
  • The product of these 6 terms would be .
  • But is a contradiction!
  • Therefore, .

Can the Determinant be 5?

  • What if ?
  • This requires at least one term to be .
  • If any , it makes exactly two terms in the expansion become .

The Parity Argument

  • With two zero terms, only 4 terms remain. Max sum .
  • If we don't use , all 6 terms are (odd numbers).
  • The sum of 6 odd numbers is always an even number.
  • Thus, .

Constructing the Maximum Matrix

  • Since 6 and 5 are impossible, the next candidate is 4.
  • Let's construct a matrix to achieve this:

Final Evaluation

  • Key Takeaway: Logical bounding is often faster than brute force.

The Sigma Insight: Properties of Determinants

Solution Diagram

Analyzing the Setup

Welcome, fellow explorers of the mathematical universe. Today, we are going to unravel a classic puzzle that often appears in the halls of JEE Advanced.
We are dealing with a matrix , where every entry is restricted to the set . Our mission is to find the maximum possible value of .
At first glance, this looks like a problem of endless combinations, but as we will see, it is a problem of elegant constraints.

The Dance of Diagonals

The Sarrus Rule
To begin, let us visualize the determinant. We use the Sarrus Rule, a beautiful geometric way to expand a matrix.
We write the first two columns next to the third and trace the diagonals. We have three main diagonals (let us call their products ) and three anti-diagonals (let us call their products ).
The determinant is simply the sum of the positive terms minus the sum of the negative terms:

The Theoretical Mirage

Why 6 is a Trap
Since each entry is , the product of any three entries is also . If we want to maximize the determinant, we might dream of a scenario where and .
This would give us . It sounds perfect, but in mathematics, if something sounds too good to be true, it usually is.

The Magical Product Trick

Here is where the genius of the problem lies. Let us multiply all six terms of our expansion together:
If you trace the diagonals carefully, you will realize that every single element of the matrix appears exactly twice in this grand product. This means the product is actually the square of the product of all nine elements:
Because this is a square of a real number, the product MUST be greater than or equal to zero. Now, let us test our theoretical maximum of 6.
If , then and . The product of these six terms would be .
But wait! We just proved the product must be . Since is not , a determinant of 6 is mathematically impossible.

The Parity Argument

Why 5 Fails
What about 5? If we do not use any zeros, all six terms are .
The sum of six odd numbers is always an even number. Since 5 is odd, it is impossible to achieve.
If we use at least one zero, the number of non-zero terms decreases, and the maximum sum we can achieve drops to 4. Thus, 5 is also out of the question.

The Final Victory

Constructing the Maximum
Since 6 and 5 are impossible, we turn our eyes to 4. Can we achieve it? Yes!
Consider the matrix:
Expanding this along the first row, we get:
We have found our maximum! The maximum possible value of is 4.
The key takeaway here is that logical bounding and parity arguments are far more powerful than brute force. Keep this in your toolkit, and you will conquer any matrix problem that comes your way.

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