Ming Yang Ong
MIT EECS — DENSO Undergraduate Research and Innovation Scholar
Quadratic unconstrained binary optimization
We aim to investigate a subclass of integer programming problems known as Quadratic Unconstrained Binary Optimization (QUBO) problems. QUBO is known to be NP-hard in the general case. However, it is quite possible to discover for specific Q methods that are efficient in practice. We aim to explore different methods to solve this problem and analyze their effectiveness on different Q. Many real world problems can be aided by efficient solutions to QUBO. For example, Ising models and graph partitioning problems can be expressed naturally as a QUBO problem. QUBO is also common in many machine learning applications, and is especially related to belief propagation in graphical models.
I like optimization, NP-hard problems and matrices.