American Institute of Mathematical Sciences.
Its objective is to promote collaboration between optimization specialists, industrial practitioners and management scientists so that important practical industrial and management problems can be addressed by the use of appropriate, recent advanced optimization techniques. It is particularly hoped that the study of these practical problems will lead to the discovery of new ideas and the development of novel methodologies in optimization.
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Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization.
List of issues Optimization.
Volume 8 1977. Currently known as.: Optimization: A Journal of Mathematical Programming and Operations Research 1985 - current. Formerly known as. Mathematische Operationsforschung und Statistik. Series Optimization 1977 - 1984. Formerly part of. Mathematische Operationsforschung und Statistik 1970 - 1976.
Optimization practice Khan Academy.
Solving optimization problems. Optimization: sum of squares. Optimization: box volume Part 1. Optimization: box volume Part 2. Optimization: cost of materials. Optimization: area of triangle square Part 1. Optimization: area of triangle square Part 2. This is the currently selected item.
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These lectures continues to cover some more advanced concepts in optimization. They introduce large neighborhood search, which often combines constraint programming and local search, and column generation which decomposes an optimization model into a master and pricing problem, using more complex variables.
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