5 papers
Beyond Smoothed Analysis: Analyzing the Simplex Method by the Book
Eleon Bach, Alexander E. Black, Sophie Huiberts +1
Narrowing the gap between theory and practice is a longstanding goal of the algorithm analysis community. To further progress our understanding of how algorithms work in practice,…
Optimal Smoothed Analysis of the Simplex Method
Eleon Bach, Sophie Huiberts
Smoothed analysis is a method for analyzing the performance of algorithms, used especially for those algorithms whose running time in practice is significantly better than what can…
An unconditional lower bound for the active-set method in convex quadratic maximization
Eleon Bach, Yann Disser, Sophie Huiberts +1
We prove that the active-set method needs an exponential number of iterations in the worst-case to maximize a convex quadratic function subject to linear constraints, regardless of…
Upper and Lower Bounds on the Smoothed Complexity of the Simplex Method
Sophie Huiberts, Yin Tat Lee, Xinzhi Zhang
The simplex method for linear programming is known to be highly efficient in practice, and understanding its performance from a theoretical perspective is an active research topic.…
A nearly optimal randomized algorithm for explorable heap selection
Sander Borst, Daniel Dadush, Sophie Huiberts +1
Explorable heap selection is the problem of selecting the th smallest value in a binary heap. The key values can only be accessed by traversing through the underlying infinite b…