International Journal of Mathematics and Mathematical Sciences
Volume 2005 (2005), Issue 9, Pages 1339-1363
doi:10.1155/IJMMS.2005.1339

Convex separable minimization problems with a linear constraint and bounded variables

Abstract

Consider the minimization problem with a convex separable objective function over a feasible region defined by linear equality $constraint(s)/linear$ inequality constraint of the form “greater than or equal to” and bounds on the variables. A necessary and sufficient condition and a sufficient condition are proved for a feasible solution to be an optimal solution to these two problems, respectively. Iterative algorithms of polynomial complexity for solving such problems are suggested and convergence of these algorithms is proved. Some convex functions, important for problems under consideration, as well as computational results are presented.