Abstract
Previous work by the present authors [1,2] has introduced the idea that problems of constrained non-linear programming, which have hitherto been treated entirely deterministically in respect of the development of solution methods, may be interpreted probabilistically and solved by appropriate methods employing entropy maximization. This paper gives formal proofs by entirely deterministic mathematical means of the results contained in the earlier work and removes the need for any probabilistic interpretation. This consequently establishes the research on a much firmer base and also implies that the probabilistic interpretation and use of entropy maximization, though no longer strictly essential, is nonetheless valid.
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References
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© 1989 Springer Science+Business Media Dordrecht
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Templeman, A.B., Xingsi, L. (1989). Maximum Entropy and Constrained Optimization. In: Skilling, J. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 36. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-7860-8_47
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DOI: https://doi.org/10.1007/978-94-015-7860-8_47
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4044-2
Online ISBN: 978-94-015-7860-8
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