TL;DR
In two-player zero-sum games, the maximum of the row player's minimum guaranteed payoff equals the minimum of the column player's maximum loss: \( \max_x \min_y x^T A y = \min_y \max_x x^T A y \).
Minimax Theorem
In two-player zero-sum games, the maximum of the row player's minimum guaranteed payoff equals the minimum of the column player's maximum loss: \( \max_x \min_y x^T A y = \min_y \max_x x^T A y \).
Why it matters for interviews
The minimax theorem is foundational for optimal decision-making under adversarial conditions. It connects to robust optimization, worst-case portfolio design, and competitive trading strategies.
Definition and Mathematical Foundation
In two-player zero-sum games, the maximum of the row player's minimum guaranteed payoff equals the minimum of the column player's maximum loss: \( \max_x \min_y x^T A y = \min_y \max_x x^T A y \).
Application in Quantitative Finance
The minimax theorem is foundational for optimal decision-making under adversarial conditions. It connects to robust optimization, worst-case portfolio design, and competitive trading strategies.
Related Terms
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