Large-scale portfolio optimization with variational neural annealing
This addresses a critical computational bottleneck in financial asset management by providing a scalable solution for mixed-integer nonlinear programs, though it is incremental as it builds on existing optimization methods.
The paper tackles large-scale portfolio optimization under real-world constraints by mapping it to an Ising-like Hamiltonian and solving it with Variational Neural Annealing (VNA), achieving near-optimal solutions for over 2,000 assets with performance comparable to state-of-the-art optimizers like Mosek and faster convergence on hard instances.
Portfolio optimization is a routine asset management operation conducted in financial institutions around the world. However, under real-world constraints such as turnover limits and transaction costs, its formulation becomes a mixed-integer nonlinear program that current mixed-integer optimizers often struggle to solve. We propose mapping this problem onto a classical Ising-like Hamiltonian and solving it with Variational Neural Annealing (VNA), via its classical formulation implemented using autoregressive neural networks. We demonstrate that VNA can identify near-optimal solutions for portfolios comprising more than 2,000 assets and yields performance comparable to that of state-of-the-art optimizers, such as Mosek, while exhibiting faster convergence on hard instances. Finally, we present a dynamical finite-size scaling analysis applied to the S&P 500, Russell 1000, and Russell 3000 indices, revealing universal behavior and polynomial annealing time scaling of the VNA algorithm on portfolio optimization problems.