Binary optimal control by trust-region steepest descent

We present a trust-region steepest descent method for dynamic optimal control problems with binary-valued integrable control functions. Our method interprets the control function as an indicator function of a measurable set and makes set-valued adjustments derived from the sublevel sets of a topological gradient function. By combining this type of update with a trust-region framework, we are able to show by theoretical argument that our method achieves asymptotic stationarity despite possible discretization errors and truncation errors during step determination. To demonstrate the practical applicability of our method, we solve two optimal control problems constrained by ordinary and partial differential equations, respectively, and one topological optimization problem.

Blade shape optimization and internal-flow characteristics of the backward non-volute centrifugal fan

The work proposed the double parameter optimization method of the non-volute centrifugal fan’s blade profile based on the steepest descent method. Total-pressure efficiency improvement at the high-flow area was taken as an optimization objective. A method of applying the steepest descent method to modify the blade profile of backward centrifugal fan is proposed in this paper. The gradient descent direction was analyzed to design the blade profile and obtain the optimal blade profile at a high-flow rate. Besides, numerical simulations were carried out to analyze the aerodynamic performance and the internal flow characteristics of the centrifugal fan by the computational fluid dynamics method. Numerical results showed that the blade profile along the gradient descent was optimized to effectively increase the total pressure and the total pressure efficiency of the original model at the high-flow rate. The steepest descent method for local optimization could improve the fan blade design.

M-Convex Function Minimization Under L1-Distance Constraint and Its Application to Dock Reallocation in Bike-Sharing System

In this paper, we consider a problem of minimizing an M-convex function under an L1-distance constraint (MML1); the constraint is given by an upper bound for L1-distance between a feasible solution and a given “center.” This is motivated by a nonlinear integer programming problem for reallocation of dock capacity in a bike-sharing system discussed by Freund et al. (2017). The main aim of this paper is to better understand the combinatorial structure of the dock reallocation problem through the connection with M-convexity and show its polynomial-time solvability using this connection. For this, we first show that the dock reallocation problem and its generalizations can be reformulated in the form of (MML1). We then present a pseudo-polynomial-time algorithm for (MML1) based on the steepest descent approach. We also propose two polynomial-time algorithms for (MML1) by replacing the L1-distance constraint with a simple linear constraint. Finally, we apply the results for (MML1) to the dock reallocation problem to obtain a pseudo-polynomial-time steepest descent algorithm and also polynomial-time algorithms for this problem. For this purpose, we develop a polynomial-time algorithm for a relaxation of the dock reallocation problem by using a proximity-scaling approach, which is of interest in its own right.

A novel [[EQUATION]] approach to shape optimisation with Lipschitz domains

This article introduces a novel method for the implementation of shape optimisation with Lipschitz domains. We propose to use the shape derivative to determine deformation fields which represent steepest descent directions of the shape functional in the $W^{1,\infty}-$ topology. The idea of our approach is demonstrated for shape optimisation of $n$-dimensional star-shaped domains, which we represent as functions defined on the unit $(n-1)$-sphere. In this setting we provide the specific form of the shape derivative and prove the existence of solutions to the underlying shape optimisation problem. Moreover, we show the existence of a direction of steepest descent in the $W^{1,\infty}-$ topology. We also note that shape optimisation in this context is closely related to the $\infty-$Laplacian, and to optimal transport, where we highlight the latter in the numerics section. We present several numerical experiments illustrating that our approach seems to be superior over existing Hilbert space methods, in particular in developing optimal shapes with corners.

Efficient Iterative Timing Recovery of Low-Density Parity-Check Decoding Metrics Using the Steepest Descent Algorithm for Satellite Communications at Low SNRs

An efficient iterative timing recovery via steepest descent of low-density parity-check (LDPC) decoding metrics is presented. In the proposed algorithm, a more accurate symbol timing synchronization is achieved at a low signal-to-noise (SNR) without any pilot symbol by maximizing the sum of the square of all soft metrics in LDPC decoding. The principle of the above-proposed algorithm is analyzed theoretically with the evolution trend of the probability mean of the soft LDPC decoding metrics by the Gaussian approximation. In addition, an efficiently approximate gradient descent algorithm is adopted to obtain excellent timing recovery with rather low complexity and global convergence. Finally, a complete timing recovery is accomplished where the proposed scheme performs fine timing capture, followed by a traditional Mueller–Müller (M&M) timing recovery, which acquires timing track. Using the proposed iterative timing recovery method, the simulation results indicate that the performance of the LDPC coded binary phase shift keying (BPSK) scheme with rather large timing errors is just within 0.1 dB of the ideal code performance at the cost of some rational computation and storage. Therefore, the proposed iterative timing recovery can be efficiently applied on occasions of the weak signal timing synchronization in satellite communications and so on.

The exact worst-case convergence rate of the gradient method with fixed step lengths for L-smooth functions

In this paper, we study the convergence rate of the gradient (or steepest descent) method with fixed step lengths for finding a stationary point of an L-smooth function. We establish a new convergence rate, and show that the bound may be exact in some cases, in particular when all step lengths lie in the interval (0, 1/L]. In addition, we derive an optimal step length with respect to the new bound.