Projected gradient trust-region method for solving nonlinear systems with convex constraints

In this paper, a projected gradient trust region algorithm for solving nonlinear equality systems with convex constraints is considered. The global convergence results are developed in a very general setting of computing trial directions by this method combining with the line search technique. Close to the solution set this method is locally Q-superlinearly convergent under an error bound assumption which is much weaker than the standard nonsingularity condition.

A CLASS OF TRUST REGION METHODS FOR LINEAR INEQUALITY CONSTRAINED OPTIMIZATION AND ITS THEORY ANALYSIS:I.ALGORITHM AND GLOBAL CONVERGENCEXIU NAIHUA

A class of trust region methods for solving linear inequality constrained problems is proposed in this paper. It is shown that the algorithm is of global convergence.The algorithm uses a version of the two-sided projection and the strategy of the unconstrained trust region methods. It keeps the good convergence properties of the unconstrained case and has the merits of the projection method. In some sense, our algorithm can be regarded as an extension and improvement of the projected type algorithm.

RESEARCH ON THE THEORY AND METHODS OF COMPREHENSIVE EVALUATION OF REGIONAL COAL RESOURCES

consists of forecasting for the total reserves of regional mineral resources and evaluation,compreIn general,based on evaluated objects and factors involved,evaluation of mineral resources hensive evaluation of regional mineral resources, and economic and technical evaluation of deposits. Here a region can be a country, a province, or a mining area, while a deposit is mainly related to a single type of mineral, although it can contain more than one types of minerals. In the past, to assess mineral resources was primarily to evaluate and forecast the total reserves of regional mineral resources. With the advancement of science, technology, and economy, the evaluation of mineral resources has developed from forecasting the reserves of them to comprehensively evaluating them on the basis of numerous factors.

A NEW DERIVATIVE FREE OPTIMIZATION METHOD BASED ON CONIC INTERPOLATION MODEL

In this paper, a new derivative free trust region method is developed based on the conic interpolation model for the unconstrained optimization. The conic interpolation model is built by means of the quadratic model function, the collinear scaling formula, quadratic approximation and interpolation. All the parameters in this model are determined by objective function interpolation condition. A new derivative free method is developed based upon this model and the global convergence of this new method is proved without any information on gradient.

Nonmonotone Adaptive Trust Region Algorithms with Indefinite Dogleg Path for Unconstrained Minimization

In this paper, we combine the nonmonotone and adaptive techniques with trust region method for unconstrained minimization problems. We set a new ratio of the actual descent and predicted descent. Then, instead of the monotone sequence, the nonmonotone sequence of function values are employed. With the adaptive technique, the radius of trust region △k can be adjusted automatically to improve the efficiency of trust region methods. By means of the Bunch-Parlett factorization, we construct a method with indefinite dogleg path for solving the trust region subproblem which can handle the indefinite approximate Hessian Bk. The convergence properties of the algorithm are established. Finally, detailed numerical results are reported to show that our algorithm is efficient.

A SUPERLINEARLY CONVERGENT TRUST REGION ALGORITHM FOR LC^1 CONSTRAINED OPTIMIZATION PROBLEMS

In this paper, a new trust region algorithm for nonlinear equality constrained LC1 optimization problems is given. It obtains a search direction at each iteration not by solving a quadratic programming subprobiem with a trust region bound, but by solving a system of linear equations. Since the computational complexity of a QP-Problem is in general much larger than that of a system of linear equations, this method proposed in this paper may reduce the computational complexity and hence improve computational efficiency. Furthermore, it is proved under appropriate assumptions that this algorithm is globally and super-linearly convergent to a solution of the original problem. Some numerical examples are reported, showing the proposed algorithm can be beneficial from a computational point of view.

Trust Region Algorithm for a Class of Nonlinear Complementarity Problem

In this paper, an ODE-type trust region algorithm for solving a class of nonlinear complementarity problems is proposed. A feature of this algorithm is that only the solution of linear systems of equations is required at each iteration, thus avoiding the need for solving a quadratic subproblem with a trust region bound. Under some conditions, it is proven that this algorithm is globally and locally superlinear convergent. The limited numerical examples show its efficiency.

A Nonmonotone Trust Region Method for Solving Symmetric Nonlinear Equations

A trust region method combining with nonmonotone technique is proposed tor solving symmetric nonlinear equations. The global convergence of the given method will be established under suitable conditions. Numerical results show that the method is interesting for the given problems.

High-temperature thermal stability of C/C−ZrC−SiC composites via region labeling method

To investigate the thermal stability of ceramic-matrix composites,three kinds of C/C−ZrC−SiC composites with different Zr/Si molar ratios were synthesized by reactive melt infiltration.Employing region labeling method,the high-temperature thermal stability of the composites was systematically studied by changing the temperature and holding time of thermal treatment.Results show that the mass loss rate of low Si composites has a growth trend with increasing temperature,and a crystal transformation from β-SiC toα-SiC occurs in the composites.In the calibrated area,SiC phase experiences Ostwald ripening and volume change with location migration,while ZrC phase experiences a re-sintering process with diffusion.Moreover,it is found that increasing temperature has a more obvious effect on the thermal stability than extending holding time,which is mainly attributed to the faster diffusion rate of atoms.

An Overview of Non.Stationary Property for Massive MIMO Channel Modeling

Massive multiple-input multiple-output(MIMO)emerges as one of the most promising technologies for 5G mobile communication systems.Compared to the conventional MIMO channel models,channel researches and measurements show that significant nonstationary properties rise in massive MIMO channels.Therefore,an accurate channel model is indispensable for the sake of massive MIMO system design and performance evaluation.This article presents an overview of methods of modeling non-stationary properties on both the array and time axes,which are mainly divided into two major categories:birth-death(BD)process and cluster visibility region(VR)method.The main concepts and theories are described,together with useful implementation guidelines.In conclusion,a comparison between these two methods is made.

GLOBAL CONVERGENCE OF A TRUST REGION ALGORITHM USING INEXACT GRADIENT FOR EQUALITY-CONSTRAINED OPTIMIZATION

A trust-region algorithm is presented for a nonlinear optimization problem of equality-constraints. The characterization of the algorithm is using inexact gradient information. Global convergence results are demonstrated where the gradient values are obeyed a simple relative error condition.

A GLOBALLY AND SUPERLINEARLY CONVERGENT TRUST REGION METHOD FOR LC^1 OPTIMIZATION PROBLEMS

A new trust region algorithm for solving convex LC 1 optimization problem is presented.It is proved that the algorithm is globally convergent and the rate of convergence is superlinear under some reasonable assumptions.

A TRUST REGION METHOD WITH A CONIC MODEL FOR NONLINEARLY CONSTRAINED OPTIMIZATION

Trust region methods are powerful and effective optimization methods. The conic model method is a new type of method with more information available at each iteration than standard quadratic-based methods. The advantages of the above two methods can be combined to form a more powerful method for constrained optimization. The trust region subproblem of our method is to minimize a conic function subject to the linearized constraints and trust region bound. At the same time, the new algorithm still possesses robust global properties. The global convergence of the new algorithm under standard conditions is established.

A New Nonmonotone Adaptive Trust Region Method

The trust region method plays an important role in solving optimization problems. In this paper, we propose a new nonmonotone adaptive trust region method for solving unconstrained optimization problems. Actually, we combine a popular nonmonotone technique with an adaptive trust region algorithm. The new ratio to adjusting the next trust region radius is different from the ratio in the traditional trust region methods. Under some appropriate conditions, we show that the new algorithm has good global convergence and superlinear convergence.

Discovery of Three Large HII Regions in the Galactic Plane

We discovered three large HⅡ regions： G148.8＋2.3, G149.5＋0.0 and G169.9＋2.0 in the Sino-German A6 cm polarization survey of the Galactic plane. They have been identified based on the flat spectral indexes and the strong infrared emission properties.

A NEW ADAPTIVE TRUST REGION ALGORITHM FOR OPTIMIZATION PROBLEMS

It is well known that trust region methods are very effective for optimization problems. In this article, a new adaptive trust region method is presented for solving uncon- strained optimization problems. The proposed method combines a modified secant equation with the BFGS updated formula and an adaptive trust region radius, where the new trust region radius makes use of not only the function information but also the gradient information. Under suitable conditions, global convergence is proved, and we demonstrate the local superlinear convergence of the proposed method. The numerical results indicate that the proposed method is very efficient.

A TRUST REGION ALGORITHM VIA BILEVEL LINEAR PROGRAMMING FOR SOLVING THE GENERAL MULTICOMMODITY MINIMAL COST FLOW PROBLEMS

This paper proposes a nonmonotonic backtracking trust region algorithm via bilevel linear programming for solving the general multicommodity minimal cost flow problems.Using the duality theory of the linear programming and convex theory,the generalized directional derivative of the general multicommodity minimal cost flow problems is derived.The global convergence and superlinear convergence rate of the proposed algorithm are established under some mild conditions.

GPU-based discrete element simulation on flow stability of flat-bottomed hopper

In this study, the flow stability of the flat-bottomed hopper was investigated via GPU-based discrete element method(DEM) simulation. With the material height inside the hopper reducing, the fluctuation of the flow rate indicates an unstable discharge. The flow regions of the unstable discharge were compared with that of the stable discharge, a key transformation zone, where the voidage showed the largest difference between unstable and stable discharge, was revealed. To identify the relevance of the key transformation zone and the hopper flow stability, the voidage variation of the key transformation zone with material height reducing was studied.A sharp increase in the voidage in the key transformation zone was considered to be the standard for judging the unstable hopper flow, and the ‘Top–Bottom effect' of the hopper was defined, which indicated the hopper flow was unstable when the hopper only had the top area and the bottom area, because the voidage of particles in the top area and the bottom area were both variables.

Did Ecological Engineering Projects Have a Significant Effect on Large-scale Vegetation Restoration in Beijing-Tianjin Sand Source Region, China？ A Remote Sensing Approach

Aiming for the restoration of degraded ecosystems, many ecological engineering projects have been implemented around the world. This study investigates the ecological engineering project effectiveness on vegetation restoration in the Beijing-Tianjin Sand Source Region(BTSSR) from 2000 to 2010 based on the rain use efficiency(RUE) trend in relation to the land cover. More than half of the BTSSR experienced a vegetation productivity increase from 2000 to 2010, with the increasing intensity being sensitive to the indicators chosen. A clear tendency towards smaller increasing areas was shown when using the net primary productivity(NPP, 51.30%) instead of the accumulated normalized difference vegetation index(59.30%). The short-term variation in the precipitation and intra-seasonal precipitation distribution had a great impact on the remote sensing-based vegetation productivity. However, the residual trends method(RESTREND) effectively eliminated this correlation, while incorporating the variance and skewness of the precipitation distribution increased the models′ ability to explain the vegetation productivity variation. The RUE combined with land cover dynamics was valid for the effectiveness assessment of the ecological engineering projects on vegetation restoration. Particularly, the result based on growing season accumulated normalized difference vegetation index(ΣNDVI) residuals was the most effective, showing that 47.39% of the BTSSR experienced vegetation restoration from 2000 to 2010. The effectiveness of the ecological engineering projects differed for each subarea and was proportional to the strength of ecological engineering. The water erosion region dominated by woodland showed the best restoration, followed by the wind-water erosion crisscross regions, while the wind erosion regions dominated by grassland showed the worst effect. Seriously degraded regions still cover more area in the BTSSR than restored regions. Therefore, more future effort should be put in restoring degraded land.

A New Nonmonotonic Trust Region Algorithm for A Class of Unconstrained Nonsmooth Optimization

This paper presents a new trust region algorithm for solving a class of composite nonsmooth optimizations. It is distinguished by the fact that this method does not enforce strict monotonicity of the objective function values at successive iterates and that this method extends the existing results for this type of nonlinear optimization with smooth, or piecewise smooth, or convex objective functions or their composition. It is proved that this algorithm is globally convergent under certain conditions. Finally, some numerical results for several optimization problems are reported which show that the nonmonotonic trust region method is competitive with the usual trust region method.