A modified three–term conjugate gradient method with sufficient descent property

A hybridization of the three–term conjugate gradient method proposed by Zhang et al. and the nonlinear conjugate gradient method proposed by Polak and Ribi`ere, and Polyak is suggested. Based on an eigenvalue analysis, it is shown that search directions of the proposed method satisfy the sufficient descent condition, independent of the line search and the objective function convexity. Global convergence of the method is established under an Armijo–type line search condition. Numerical experiments show practical efficiency of the proposed method.

An Adaptive Three-Term Conjugate Gradient Method with Sufficient Descent Condition and Conjugacy Condition

In this paper,an adaptive three-term conjugate gradient method is proposed for solving unconstrained problems,which generates sufficient descent directions at each iteration.Different from the existent methods,a dynamical adjustment between Hestenes–Stiefel and Dai–Liao conjugacy conditions in our proposed method is developed.Under mild condition,we show that the proposed method converges globally.Numerical experimentation with the new method indicates that it efficiently solves the test problems and therefore is promising.

A Descent Gradient Method and Its Global Convergence

Y Liu and C Storey(1992)proposed the famous LS conjugate gradient method which has good numerical results.However,the LS method has very weak convergence under the Wolfe-type line search.In this paper,we give a new descent gradient method based on the LS method.It can guarantee the sufficient descent property at each iteration and the global convergence under the strong Wolfe line search.Finally,we also present extensive preliminary numerical experiments to show the efficiency of the proposed method by comparing with the famous PRP^+method.

A New Nonlinear Conjugate Gradient Method for Unconstrained Optimization Problems

In this paper,an efficient conjugate gradient method is given to solve the general unconstrained optimization problems,which can guarantee the sufficient descent property and the global convergence with the strong Wolfe line search conditions.Numerical results show that the new method is efficient and stationary by comparing with PRP＋ method,so it can be widely used in scientific computation.

A NEW DESCENT MEMORY GRADIENT METHOD AND ITS GLOBAL CONVERGENCE

In this article, a new descent memory gradient method without restarts is proposed for solving large scale unconstrained optimization problems. The method has the following attractive properties： 1） The search direction is always a sufficiently descent direction at every iteration without the line search used; 2） The search direction always satisfies the angle property, which is independent of the convexity of the objective function. Under mild conditions, the authors prove that the proposed method has global convergence, and its convergence rate is also investigated. The numerical results show that the new descent memory method is efficient for the given test problems.

Evaluation of Burden Descent Model for Burden Distribution in Blast Furnace

Mathematical models for burden descending process have been applied to obtain whole burden structures in blast furnace,whereas the accuracy of those burden descent models has not been sufficiently investigated.Special evaluation method based on timeline burden profiles was established to quantitatively evaluate the error between experimental and modeled burden structures.Four existing burden descent models were utilized to describe the burden structure of a 1/20 scaled warm blast furnace.Input modeling conditions including initial burden profile,descending volumes in each time interval,and normalized descending velocity distribution were determined via special image processing technology.Modeled burden structures were evaluated combined with the published experimental data.It is found that all the models caught the main profile of the burden structure.Furthermore,the improved nonuniform descent model（Model IV）shows the highest level of precision especially when burden descends with unstable velocity distribution tendency.Meanwhile,the traditional nonuniform descent model（Model III）may also be desirable to model the burden descending process when the burden descending velocity presents a linear tendency.Finally,the uniform descent model（Model I）might be the first option for roughly predicting burden structure.

A Modified Hestenes-Stiefel Conjugate Gradient Method and Its Convergence

It is well-known that the direction generated by Hestenes-Stiefel （HS） conjugate gradient method may not be a descent direction for the objective function. In this paper, we take a little modification to the HS method, then the generated direction always satisfies the sufficient descent condition. An advantage of the modified Hestenes-Stiefel （MHS） method is that the scalar βκHS. keeps nonnegative under the weak Wolfe-Powell line search. The global convergence result of the MHS method is established under some mild conditions. Preliminary numerical results show that the MHS method is a little more efficient than PRP and HS methods.

A NEW ADAPTIVE SUBSPACE MINIMIZATION THREE-TERM CONJUGATE GRADIENT ALGORITHM FOR UNCONSTRAINED OPTIMIZATION

A new adaptive subspace minimization three-term conjugate gradient algorithm with nonmonotone line search is introduced and analyzed in this paper.The search directions are computed by minimizing a quadratic approximation of the objective function on special subspaces,and we also proposed an adaptive rule for choosing different searching directions at each iteration.We obtain a significant conclusion that the each choice of the search directions satisfies the sufficient descent condition.With the used nonmonotone line search,we prove that the new algorithm is globally convergent for general nonlinear functions under some mild assumptions.Numerical experiments show that the proposed algorithm is promising for the given test problem set.