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Samuel Liew

Publications and source records attributed to Samuel Liew.

2 recordsLinked to original sources

The Bardos-Tartar Conjecture for the Navier-Stokes Equations

We consider the incompressible viscous Navier-Stokes equations in dimension $d=2,3$ with periodic boundary conditions and time-independent external forcing. When $d=2$, the Bardos-Tartar conjecture asks whether the set of solutions at any given positive time is dense in the Hilbert subspace of $L^2$ consisting of mean-zero divergence-free vector fields, with respect to the strong $L^2$ norm. We prove the Bardos-Tartar conjecture and, more generally, its analogue for the hyperviscous Navier-Stokes equations studied by Guo-Titi. In addition, for $d=2,3$ we show that any given initial condition is either backwards-extendable (i) for all values of viscosity $ν>0$, or (ii) only for viscosities that belong to a Lebesgue-null subset of $\mathbb{R}_+$. When (i) holds, we show the initial condition and forcing must be real-analytic (in fact, both belong to $D(e^{σA})$ for some $σ>0$, where $A$ is the Stokes operator). Furthermore, we show that (i) is extremely unstable under perturbations. In particular, given any initial condition we show that two Fourier modes can be perturbed so that (ii) holds.

math.AP↗

Model Predictive Control For Trade Execution

We address the problem of executing large client orders in continuous double-auction markets under time and liquidity constraints. We propose a model predictive control (MPC) framework that balances three competing objectives: order completion, market impact, and opportunity cost. Our algorithm is guided by a trading schedule (such as time-weighted average price or volume-weighted average price) but allows for deviations to reduce the expected execution cost, with due regard to risk. Our MPC algorithm executes the order progressively, and at each decision step it solves a fast quadratic program that trades off expected transaction cost against schedule deviation, while incorporating a residual cost term derived from a simple base policy. Approximate schedule adherence is maintained through explicit bounds, while variance constraints on deviation provide direct risk control. The resulting system is modular, data-driven, and suitable for deployment in production trading infrastructure. Using six months of NASDAQ 'level 3' data and simulated orders, we show that our MPC approach reduces schedule shortfall by approximately 40-50% relative to spread-crossing benchmarks and achieves significant reductions in slippage. Moreover, augmenting the base policy with predictive price information further enhances performance, highlighting the framework's flexibility for integration with forecasting components.

q-fin.TR↗