High-Order Decoupled and Bound Preserving Local Discontinuous Galerkin Methods for a Class of Chemotaxis Models

In this paper,we explore bound preserving and high-order accurate local discontinuous Galerkin(LDG)schemes to solve a class of chemotaxis models,including the classical Keller-Segel(KS)model and two other density-dependent problems.We use the convex splitting method,the variant energy quadratization method,and the scalar auxiliary variable method coupled with the LDG method to construct first-order temporal accurate schemes based on the gradient flow structure of the models.These semi-implicit schemes are decoupled,energy stable,and can be extended to high accuracy schemes using the semi-implicit spectral deferred correction method.Many bound preserving DG discretizations are only worked on explicit time integration methods and are difficult to get high-order accuracy.To overcome these difficulties,we use the Lagrange multipliers to enforce the implicit or semi-implicit LDG schemes to satisfy the bound constraints at each time step.This bound preserving limiter results in the Karush-Kuhn-Tucker condition,which can be solved by an efficient active set semi-smooth Newton method.Various numerical experiments illustrate the high-order accuracy and the effect of bound preserving.

Global convergent algorithm for the bilevel linear fractional-linear programming based on modified convex simplex method

A global convergent algorithm is proposed to solve bilevel linear fractional-linear programming, which is a special class of bilevel programming. In our algorithm, replacing the lower level problem by its dual gap equaling to zero, the bilevel linear fractional-linear programming is transformed into a traditional sin- gle level programming problem, which can be transformed into a series of linear fractional programming problem. Thus, the modi- fied convex simplex method is used to solve the infinite linear fractional programming to obtain the global convergent solution of the original bilevel linear fractional-linear programming. Finally, an example demonstrates the feasibility of the proposed algorithm.

A higher-order porous thermoelastic problem with microtemperatures

In this paper,we study a porous thermoelastic problem with microtemperatures assuming parabolic higher order in time derivatives for the thermal variables.The model is derived and written as a coupled linear system.Then,a uniqueness result is proved by using the logarithmic convexity method in the case that we do not assume that the mechanical energy is positive definite.Finally,the existence of the solution is obtained by introducing an energy function and applying the theory of linear semigroups.

ON ALTERNATIVE OPTIMAL SOLUTIONS TO QUASIMONOTONIC PROGRAMMING WITH LINEAR CONSTRAINTS

In this paper, the nonlinear programming problem with quasimonotonic （ both quasiconvex and quasiconcave ）objective function and linear constraints is considered. With the decomposition theorem of polyhedral sets, the structure of optimal solution set for the programming problem is depicted. Based on a simplified version of the convex simplex method, the uniqueness condition of optimal solution and the computational procedures to determine all optimal solutions are given, if the uniqueness condition is not satisfied. An illustrative example is also presented.

Multiple optimal solutions to a sort of nonlinear optimization problem

The optimization problem is considered in which the objective function is pseudolinear（both pseudoconvex and pseudoconcave） and the constraints are linear. The general expression for the optimal solutions to the problem is derived with the representation theorem of polyhedral sets, and the uniqueness condition of the optimal solution and the computational procedures to determine all optimal solutions （if the uniqueness condition is not satisfied ） are provided. Finally, an illustrative example is also given.

A BRANCH BOUND METHOD FOR SUBSET SUM PROBLEM

This paper indicates the possible difficulties for applying the interior point method to NPcomplete problems,transforms an NP-complete problem into a nonconvex quadratic program and then develops some convexity theories for it. Lastly it proposes an algorithm which uses Karmarkar's algorithm as a subroutine. The finite convergence of this algorithm is also proved.

Combining Clustered Adaptive Multistart and Discrete Dynamic Convexized Method for the Max-Cut Problem

Given an undirected graph with edge weights,the max-cut problem is to find a partition of the vertices into twosubsets,such that the sumof theweights of the edges crossing different subsets ismaximized.Heuristics based on auxiliary function can obtain high-quality solutions of the max-cut problem,but suffer high solution cost when instances grow large.In this paper,we combine clustered adaptive multistart and discrete dynamic convexized method to obtain high-quality solutions in a reasonable time.Computational experiments on two sets of benchmark instances from the literature were performed.Numerical results and comparisons with some heuristics based on auxiliary function show that the proposed algorithm is much faster and can obtain better solutions.Comparisons with several state-ofthe-science heuristics demonstrate that the proposed algorithm is competitive.

SEQUENTIAL CONVEX PROGRAMMING METHODS FOR SOLVING LARGE TOPOLOGY OPTIMIZATION PROBLEMS： IMPLEMENTATION AND COMPUTATIONAL RESULTS

In this paper, we describe a method to solve large-scale structural optimization problems by sequential convex programming （SCP）. A predictor-corrector interior point method is applied to solve the strictly convex subproblems. The SCP algorithm and the topology optimization approach are introduced. Especially, different strategies to solve certain linear systems of equations are analyzed. Numerical results are presented to show the efficiency of the proposed method for solving topology optimization problems and to compare different variants.

Multivariate spectral estimation based on THREE

Power spectrum estimation is to use the limited length of data to estimate the power spectrum of the signal. In this paper, we study the recently proposed tunable high-resolution estimator（THREE）, which is based on the best approximation to a given spectrum, with respect to different notions of distance between power spectral densities. We propose and demonstrate a different distance for the optimization part to estimate the multivariate spectrum. Its effectiveness is tested through Matlab simulation. Simulation shows that our approach constitutes a valid estimation procedure. And we also demonstrate the superiority of the method, which is more reliable and effective compared with the standard multivariate identification techniques.