CHAP's cross-platform portfolio finds feasible solutions for 47 of 50 MIP benchmark instances in five minutes, beating Gurobi (44) and cuOpt (43) by coordinating GPU tabu search with CPU fix-and-propagate and feasibility pump via a shared pool.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Introduces a beam-search heuristic for random subset sum that uses meshing to obtain inverse-quadratic expected error decay in linearithmic time.
A digital twin framework integrates agent-based decision support and metaheuristic optimization to dynamically model and optimize EV charging infrastructure, policies, and renewables in a Hanoi university campus setting.
citing papers explorer
-
CHAP: A Hybrid GPU-CPU Heuristic for MIP
CHAP's cross-platform portfolio finds feasible solutions for 47 of 50 MIP benchmark instances in five minutes, beating Gurobi (44) and cuOpt (43) by coordinating GPU tabu search with CPU fix-and-propagate and feasibility pump via a shared pool.
-
Inverse Quadratic Decay in Random Subset Sum
Introduces a beam-search heuristic for random subset sum that uses meshing to obtain inverse-quadratic expected error decay in linearithmic time.
-
A Digital Twin Framework for Decision-Support and Optimization of EV Charging Infrastructure in Localized Urban Systems
A digital twin framework integrates agent-based decision support and metaheuristic optimization to dynamically model and optimize EV charging infrastructure, policies, and renewables in a Hanoi university campus setting.