Convex quadratic programming with one constraint and bounded variables
Mathematical Programming: Series A and B
A finite algorithm for finding the projection of a point onto the Canonical simplex of Rn
Journal of Optimization Theory and Applications
Quasi-Newton updates with bounds
SIAM Journal on Numerical Analysis
An algorithm for a singly constrained class of quadratic programs subject to upper and lower bounds
Mathematical Programming: Series A and B
On the solution of concave knapsack problems
Mathematical Programming: Series A and B
On the continuous quadratic knapsack problem
Mathematical Programming: Series A and B
Local minima for indefinite quadratic knapsack problems
Mathematical Programming: Series A and B
Mathematical Programming: Series A and B
Convex programming with single separable constraint and bounded variables
Computational Optimization and Applications
Separable Convex Optimization Problems with Linear Ascending Constraints
SIAM Journal on Optimization
Optimal Traffic Sharing in GERAN
Wireless Personal Communications: An International Journal
Network denoising in social media
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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A minimization problem with convex and separable objective function subject to a separable convex inequality constraint “≤” and bounded variables is considered. A necessary and sufficient condition is proved for a feasible solution to be an optimal solution to this problem. Convex minimization problems subject to linear equality/linear inequality “≥” constraint, and bounds on the variables are also considered. A necessary and sufficient condition and a sufficient condition, respectively, are proved for a feasible solution to be an optimal solution to these two problems. Algorithms of polynomial complexity for solving the three problems are suggested and their convergence is proved. Some important forms of convex functions and computational results are given in the Appendix.