Sparse Resultant

Sparse resultants are mathematical tools used to efficiently solve systems of polynomial equations with many zero coefficients, a common scenario in computer vision and other fields. Current research focuses on improving the efficiency of algorithms based on sparse resultants, often comparing them to alternative methods like Gröbner basis techniques and the action matrix method, particularly for minimal problem solving in computer vision applications. This involves exploring optimized iterative schemes and incorporating additional polynomials to reduce computational complexity and improve solver stability. The development of more efficient sparse resultant solvers has significant implications for various applications requiring the robust and rapid solution of large, sparse polynomial systems.

Papers

October 28, 2024

Efficient Training of Sparse Autoencoders for Large Language Models via Layer Groups
Davide Ghilardi, Federico Belotti, Marco Molinari
Multi Layer Efficient Training Interpretable Feature Sparse Autoencoders Intermediate Layer Sparse Resultant

July 15, 2023

Sharp Convergence Rates for Matching Pursuit
Jason M. Klusowski, Jonathan W. Siegel
Lower Bound Worst Case Greedy Algorithm Pursuit Algorithm Sparse Resultant

January 16, 2023

Sparse resultant based minimal solvers in computer vision and their connection with the action matrix
Snehal Bhayani, Janne Heikkilä, Zuzana Kukelova
Computer Vision New Connection Minimal Problem Polynomial Bound Sparse Resultant

Sparse Resultant

Papers

Efficient Training of Sparse Autoencoders for Large Language Models via Layer Groups

Sharp Convergence Rates for Matching Pursuit

Sparse resultant based minimal solvers in computer vision and their connection with the action matrix