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Lecture
Understanding Complexity: Algorithms and NP Problems
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Related lectures (32)
Introduction to Complexity
Introduces time complexity and worst-case analysis of algorithms, abstracting computational complexity from implementation details.
Computational Complexity: Theory and Applications
Explores computational complexity, NP-completeness, and polynomial reductions in theoretical computer science.
Elements of computational complexity
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Covers classical and quantum computational complexity concepts and implications.
Theory of Computation: Decidability and Complexity
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Delves into the theory of computation, covering decidability, complexity, P vs. NP, and reductions.
Optimization Algorithms
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Covers optimization algorithms, convergence properties, and time complexity of sequences and functions.
Dynamic Programming: Knapsack
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Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.
Theory of Computation: Countability and Undecidable Problems
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Explores countability and undecidable problems in the theory of computation.
Computation & Algorithms I
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Explores algorithm complexity, time complexity estimation, Landau notations, and asymptotic behavior analysis.
Subset Sum: LLL Algorithm
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Covers the Subset Sum problem and the efficient LLL algorithm for finding solutions in lattice basis reduction.
Discrete Mathematics: Logic, Structures, Algorithms
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Covers the basics of discrete mathematics, focusing on logic, structures, and algorithms for computer systems.
Optimal Least Square Adaptive Filter: FIR Filter
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Explores the Optimal Least Square Adaptive Filter, focusing on the Finite Impulse Response (FIR) Filter and strategies to reduce computational burden.
Derivatives, O-Notation
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Explores derivatives, O-Notation, extrema, and algorithm complexity in Analysis 1.
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