Adaptive Optimal Load Balancing in a Nonhomogeneous Multiserver System with a Central Job Scheduler
IEEE Transactions on Computers
Convex Separable Minimization Subject to Bounded Variables
Computational Optimization and Applications
Breakpoint searching algorithms for the continuous quadratic knapsack problem
Mathematical Programming: Series A and B
Power minimization for CDMA under colored noise
IEEE Transactions on Communications
Decentralized sequential change detection using physical layer fusion
IEEE Transactions on Wireless Communications - Part 1
Capacity scaling in MIMO wireless systems under correlated fading
IEEE Transactions on Information Theory
Optimal sequences for CDMA under colored noise: a Schur-saddle function property
IEEE Transactions on Information Theory
Approximately universal codes over slow-fading channels
IEEE Transactions on Information Theory
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Separable convex optimization problems with linear ascending inequality and equality constraints are addressed in this paper. An algorithm that explicitly characterizes the optimum point in a finite number of steps is described. The optimum value is shown to be monotone with respect to a partial order on the constraint parameters. Moreover, the optimum value is convex with respect to these parameters. This work generalizes the existing algorithms of Morton, von Randow, and Ringwald [Math. Programming, 32 (1985), pp. 238-241] and Viswanath and Anantharam [IEEE Trans. Inform. Theory, 48 (2002), pp. 1295-1318] to a wider class of separable convex objective functions. Computational experiments that compare the proposed algorithm with a standard convex optimization tool are also provided.