The minimum information principle for discriminative learning
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Geometric programming for communication systems
Communications and Information Theory
A channel model for inferring the optimal number of electrodes for future cochlear implants
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
Computation of channel capacity based on self-concordant functions
Journal of Electrical and Computer Engineering
Wireless Personal Communications: An International Journal
Holistic design parameter optimization of multiple periodic resources in hierarchical scheduling
Proceedings of the Conference on Design, Automation and Test in Europe
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We show that the Lagrange dual problems of the channel capacity problem with input cost and the rate distortion problem are simple geometric programs. Upper bounds on channel capacity and lower bounds on rate distortion can be efficiently generated from their duals. For channel capacity, the geometric programming dual characterization is shown to be equivalent to the minmax Kullback-Leibler (KL) characterization in Csiszar et al. (1981). For rate distortion, the geometric programming dual is extended to rate distortion with two-sided state information. A "duality by mapping" is then given between the Lagrange dual problems of channel capacity with input cost and rate distortion, which resolves several apparent asymmetries between their primal problems in the familiar form of mutual information optimization problems. Both the primal and dual problems can be interpreted in a common framework of free energy optimization from statistical physics.