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Zuodi Xie

Publications and source records attributed to Zuodi Xie.

2 recordsLinked to original sources

Extrema of cooling branching Brownian motion and related Gaussian fields

We introduce a family of Gaussian fields with an inhomogeneous cooling variance profile, indexed by $\alpha\in(0,1/2]$, whose covariance is log--log-correlated at $\alpha=1/2$ and approaches the log-correlated regime as $\alpha\downarrow0$. We consider both one dimensional such objects (which we call {\it cooling Branching Brownian Motions}) as well as the related Gaussian fields. We identify the centering of the maximum at the terminal time $T$ and prove tightness of the recentered maximum. While the exponent in the first-order growth varies linearly with $\alpha$, giving a leading order of $T^{1-\alpha}$, the second-order correction exhibits a phase transition at $\alpha=1/3$. We also show that any subsequential limit cannot be a randomly shifted Gumbel law, in contrast with the logarithmically correlated case.

math.PR

Discrete LQR and ILQR methods based on high order Runge-Kutta methods

In this paper, discrete linear quadratic regulator (DLQR) and iterative linear quadratic regulator (ILQR) methods based on high-order Runge-Kutta (RK) discretization are proposed for solving linear and nonlinear quadratic optimal control problems respectively. As discovered in [W. Hager, Runge-Kutta method in optimal control and the discrete adjoint system, Numer. Math.,2000, pp. 247-282], direct approach with RK discretization is equivalent with indirect approach based on symplectic partitioned Runge-Kutta (SPRK) integration. In this paper, we will reconstruct this equivalence by the analogue of continuous and discrete dynamic programming. Then, based on the equivalence, we discuss the issue that the internal-stage controls produced by direct approach may have lower order accuracy than the RK method used. We propose order conditions for internal-stage controls and then demonstrate that third or fourth order explicit RK discretization cannot avoid the order reduction phenomenon. To overcome this obstacle, we calculate node control instead of internal-stage controls in DLQR and ILQR methods. And numerical examples will illustrate the validity of our methods. Another advantage of our methods is high computational efficiency which comes from the usage of feedback technique. In this paper, we also demonstrate that ILQR is essentially a quasi-Newton method with linear convergence rate.

math.NA