4 papers
Heuristics for Combinatorial Optimization via Value-based Reinforcement Learning: A Unified Framework and Analysis
Orit Davidovich, Shimrit Shtern, Segev Wasserkrug +1
Since the 1990s, considerable empirical work has been carried out to train statistical models, such as neural networks (NNs), as learned heuristics for combinatorial optimization (…
Finding Probably Approximate Optimal Solutions by Training to Estimate the Optimal Values of Subproblems
Nimrod Megiddo, Segev Wasserkrug, Orit Davidovich +1
The paper is about developing a solver for maximizing a real-valued function of binary variables. The solver relies on an algorithm that estimates the optimal objective-function va…
Spline Quantile Regression
Ta-Hsin Li, Nimrod Megiddo
Quantile regression is a powerful tool capable of offering a richer view of the data as compared to least-squares regression. Quantile regression is typically performed individuall…
On the Complexity of Combinatorial Optimization on Fixed Structures
Nimrod Megiddo
Combinatorial optimization can be described as the problem of finding a feasible subset that maximizes a objective function. The paper discusses combinatorial optimization problems…