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on
1
Intro
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Optimistic Expanded Network
3
Cycles
4
Refinement
5
Example
6
Time windows
7
Label algorithms
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An example
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A toy example
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Results
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Prepartitioning
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Multiplication
13
Complexity
14
Recursion example
15
Optimal solution
16
Conclusions
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore an iterative algorithm for solving dynamic programs of pseudo-polynomial complexity in this GERAD Research Center DS4DM Coffee Talk. Delve into the intricacies of handling large magnitudes as Claudio Contardo from Concordia University presents a comprehensive approach. Learn about optimistic expanded networks, cycles, refinement techniques, and time windows. Discover label algorithms through practical examples, including a toy example to illustrate key concepts. Examine the impact of prepartitioning, multiplication, and complexity on the algorithm's performance. Gain insights into recursion examples and optimal solution strategies. Conclude with a thorough understanding of this innovative method for tackling challenging dynamic programming problems.

On the Solution of Pseudo-Polynomial Dynamic Programs Involving Large Magnitudes

GERAD Research Center
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