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A Fast Wang kWTA With Application in Sealed-Bid Uniform Price Auction
IEEE Transactions on Neural Networks and Learning Systems
|August 21, 2025
Summary
Two novel algorithms, Fast Wang k-winner-take-all (kWTA), enhance sealed-bid auctions. These methods use a bisection update for faster, more secure winner and price determination, even with unencrypted data.
Area of Science:
- Computational Mathematics
- Auction Theory
- Algorithm Design
Background:
- Traditional k-winner-take-all (kWTA) algorithms, like the original Wang kWTA, often require numerous iterations for convergence.
- Gradient descent methods used in kWTA can be computationally intensive and slow for real-time applications.
Purpose of the Study:
- To introduce two novel discrete-time algorithms, Fast Wang kWTA, for efficient computation in sealed-bid auctions.
- To improve the speed and robustness of winner and price determination in uniform price auctions.
Main Methods:
- Developed two Fast Wang kWTA algorithms utilizing a bisection method for state updates, replacing traditional gradient descent.
- Analyzed the iteration complexity, demonstrating independence from input numbers and learning step size.
Main Results:
- The Fast Wang kWTA algorithms significantly reduce the number of iterations required for convergence compared to the original Wang kWTA.
- Algorithm performance is primarily dependent on the initial guess of the maximum input value, not the scale of the input data.
Conclusions:
- The Fast Wang kWTA algorithms are highly suitable for real-time, distributed winner and price determination in sealed-bid uniform price auctions.
- These algorithms offer enhanced bidding price protection, maintaining security even with unencrypted data transmission.
