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A Dual Optimization Framework for Some Problems of Information Theory and Statistics.
TEXAS UNIV AT AUSTIN CENTER FOR CYBERNETIC STUDIES
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A new dual optimization framework for some problems of information theory and statistics is developed in the form of dual convex programming problems and their duality theory. It extends the work for finite discrete distributions to the case of general measures. Although the primal problem constrained relative entropy is an infinite dimensional one, the dual problem is a finite dimensional one without constraints and involving only exponential and linear terms. Applications range from mathematical statistics and statistical mechanics to traffic engineering, marketing and economics.
APPROVED FOR PUBLIC RELEASE