large scale optimization problems 大规模优化问题
large scale linear programming problems 大规模线性规划问题
large scale pattern recognition problems 大规模模式识别问题
Alternating direction methods are suitable ones for solving large-scale problems.
交替方向法适合于求解大规模问题。
Based on a rank-1 update, we propose sparse Bayesian Learning Algorithm (SBLA), which has low complexity and high sparseness, thus being very suitable for large-scale problems.
基于秩- 1更新,提出了稀疏贝叶斯学习算法(SBLA)。该算法具有较低的计算复杂度和较高的稀疏性,从而适合于求解大规模问题。
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