In this paper, a class of new descent algorithm is proposed to solve unconstrained optimization problems.
研究给出了一类新的求解无约束优化问题的下降算法。
The conjugate gradient method is one of the most efficient methods for solving unconstrained optimization problems.
共轭梯度法是求解无约束优化问题的一类有效方法。
A nonmonotonic trust region algorithm with line search for unconstrained optimization problems is presented in this paper.
给出无约束最优化的一类带线搜索的非单调信赖域算法。
By using a modified BFGS formula, a BFGS-type trust region method with line search technique for unconstrained optimization problems is proposed.
利用一个修正的BFGS公式，提出了结合线搜索技术的BFGS -信赖域方法，并在一定条件下证明了该方法的全局收敛性和超线性收敛性。
This paper presents a non-monotone curve search method for unconstrained optimization problems and proves its convergence under some mild conditions.
本文提出一类求解无约束优化问题的非单调曲线搜索方法，在较弱条件下证明了其收敛性。
Pr conjugate gradient method is one of the efficient methods for solving large scale unconstrained optimization problems, however, its global convergence has not been solved for a long time.
PR共轭梯度法是求解大型无约束优化问题的有效算法之一，但是算法的全局收敛性在理论上一直没有得到解决。
Trust region method is a kind of efficient methods to solve the general unconstrained optimization and its special situation, the nonlinear least squares problems.
对于一般的无约束最优化问题及其特殊情况非线性最小二乘问题而言，信赖域方法是一种有效的方法。
A new filled function with one parameter is used to find a global minimizer for unconstrained global optimization problems.
考虑用单参数填充函数求解无约束全局优化问题。
A novel algorithm is proposed to deal with both unconstrained and constrained numerical optimization problems.
针对数值优化问题，提出了组织进化数值优化算法。
We discuss non-monotonic trust region algorithm for unconstrained nonlinear optimization problems in view of Trust Region Model mainly.
然后从信赖域子问题的角度出发，对无约束优化问题提出了一个改进的非单调信赖域算法。
Conjugate gradient method is an efficient method in solving problems with unconstrained optimization, which is especially efficient in dealing large dimension.
共轭梯度法是求解大规模无约束优化问题的一种有效方法。
Particle swarm optimization (PSO) algorithm is one of the most powerful methods for solving unconstrained and constrained global optimization problems.
粒子群优化算法(PSO)是一种有效的随机全局优化技术。
The M-elite coevolutionary algorithm (MECA) is proposed for high-dimensional unconstrained numerical optimization problems based on the concept of coevolutionary algorithm and elitist strategy.
本文在其基础上考虑了每个零部件的需求量不同所带来的影响，提出了一种基于精英策略和自适应性的混合遗传算法。
The M-elite coevolutionary algorithm (MECA) is proposed for high-dimensional unconstrained numerical optimization problems based on the concept of coevolutionary algorithm and elitist strategy.
本文在其基础上考虑了每个零部件的需求量不同所带来的影响，提出了一种基于精英策略和自适应性的混合遗传算法。
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