17 citations · 34 across the 6 of their papers we have counts for
6 papers
Nonlocal Monte Carlo via Reinforcement Learning
Dmitrii Dobrynin, Masoud Mohseni, John Paul Strachan
Optimizing or sampling complex cost functions of combinatorial optimization problems is a longstanding challenge across disciplines and applications. When employing family of conve…
Accelerating Hybrid XORCNF Boolean Satisfiability Problems Natively with In-Memory Computing
Haesol Im, Fabian Böhm, Giacomo Pedretti +14
The Boolean satisfiability (SAT) problem is a computationally challenging decision problem central to many industrial applications. For SAT problems in cryptanalysis, circuit desig…
A Statistical Analysis for Per-Instance Evaluation of Stochastic Optimizers: Avoiding Unreliable Conclusions
Moslem Noori, Elisabetta Valiante, Thomas Van Vaerenbergh +2
A key trait of stochastic optimizers is that multiple runs of the same optimizer in attempting to solve the same problem can produce different results. As a result, their performan…
Hardware-Compatible Single-Shot Feasible-Space Heuristics for Solving the Quadratic Assignment Problem
Haesol Im, Chan-Woo Yang, Moslem Noori +10
Research into the development of special-purpose computing architectures designed to solve quadratic unconstrained binary optimization (QUBO) problems has flourished in recent year…
Solving Boolean satisfiability problems with resistive content addressable memories
Giacomo Pedretti, Fabian Böhm, Tinish Bhattacharya +15
Solving optimization problems is a highly demanding workload requiring high-performance computing systems. Optimization solvers are usually difficult to parallelize in conventional…
Energy landscapes of combinatorial optimization in Ising machines
Dmitrii Dobrynin, Adrien Renaudineau, Mohammad Hizzani +3
Physics-based Ising machines (IM) have been developed as dedicated processors for solving hard combinatorial optimization problems with higher speed and better energy efficiency. G…