Separable convex optimization problems with linear ascending constraints

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. Key words. ascending constraints, convex optimization, linear constraints, separable problem AMS subject classifications. 90C25, 52A41 DOI. 10.1137/07069729X

• Type: Journal
• Year: 2007
• Where: CORR
• Authors: Arun Padakandla, Rajesh Sundaresan
• Keywords: Convex Optimization, CORR 2007, Education, Separable Convex, Separable Convex Optimization