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Hjortland, Andrew L., author.
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programming. Using the optimal solution as a baseline, suboptimal service decision-making strategies were
by
Chen, Tao, author.
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-performance accelerators using familiar parallel programming paradigms, without needing to know low-level hardware design
by
Aziz, Jonathan David, author.
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differential dynamic programming (DDP). The change of variable from time to orbit anomaly is accomplished by a
by
Lin, Nan, author.
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a sparse dynamic Bayesian network model coupled with integer programming for causal inference in
by
Crose, Marquis Grant, author.
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efficient parallel programming scheme allows for significantly shortened computational times and solutions
by
Khan, Najam, author.
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models. Next, a methodology, based on dynamic programming, was formulated by combing the EPA's pollutant
by
Habib, Abdulelah, author.
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- such as motors or pumps, steel manufacturing, and data centers. Mixed integer linear programming was
by
Ma, Guangrui, author.
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different stations. An approximated dynamic policy is generated based on linear programming approach.
by
Harper, Robert.
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. Generic programming; Part VI. Infinite Data Types: 18. Inductive and co-inductive types; 19. Recursive
by
Hooker, John N.
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material in linear, nonlinear, and dynamic programming.</li><li>Network flow theory, due to its importance
by
Hwu, Wen-mei.
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Simulations 25 Financial Market Value-at-Risk Estimation using the Monte Carlo Method Part 6: Programming
by
Sieniutycz, Stanislaw.
ScienceDirect https://www.sciencedirect.com/science/book/9780080451411
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for deterministic and stochastic optimization approaches based on: nonlinear programming, dynamic