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Dynamic Programming With Python
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Author(s): Gurram Sunitha (School of Computing, Mohan Babu University, Tirupati, India), Arman Abouali (Technical University Clausthal, Germany), Mohammad Gouse Galety (Samarkand International University of Technology, Uzbekistan)and A. V. Sriharsha (Mohan Babu University, India)
Copyright: 2023
Pages: 21
Source title:
Advanced Applications of Python Data Structures and Algorithms
Source Author(s)/Editor(s): Mohammad Gouse Galety (Department of Computer Science, Samarkand International University of Technology, Uzbekistan), Arul Kumar Natarajan (Samarkand International University of Technology, Uzbekistan)and A. V. Sriharsha (MB University, India)
DOI: 10.4018/978-1-6684-7100-5.ch005
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Abstract
Algorithms are at the heart of computer programming. They form the basis of all software applications and help to solve complex computational problems. Various problem-solving strategies involve divide and conquer, greedy, recursion, dynamic programming, backtracking, etc. This can be used to solve optimization problems that have overlapping subproblems. It aims to find an optimal solution by breaking down a problem into sub-problems in order to manage the complexity of the problem-solving and remembering their solutions in order to avoid repeating computation time in future steps. Mathematical optimization is a crucial component of dynamic programming, and it allows us to efficiently solve a wide range of problems that involve making decisions over time. This chapter discusses dynamic programming's relevance, mathematical optimization, ability to solve a wide range of issues, important qualities, and top-down and bottom-up problem-solving methodologies. Dynamic programming solves some typical computational problems effectively, and Python code is supplied for reference.
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