Pseudo-polynomial time

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Delves into complexity theory, focusing on the P vs NP problem and the classification of computational problems based on efficiency.

Explores critical phenomena in complex systems, including stochastic objects, percolation, and combinatorial optimization.

Covers the density operator formalism, polynomial time, NP problems, BPP, QMA, and probabilistic algorithms.

Delves into the theory of computation, covering decidability, complexity, P vs. NP, and reductions.

Covers quantum algorithms, complexity classes, Grover's algorithm, and quantum information in computational complexity.

Introduces the FPTAS for the Knapsack problem, focusing on achieving an approximation of (1-ε) times the optimal solution.

Explores the theory of computability, decision problems, complexity classes, and the 'P vs. NP' conundrum.

Explores the theory of computation, undecidable problems, complexity measurement, and the P complexity class.

Explores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.

Explores the Ford Fulkerson algorithm, Max-Flow Min-Cut theorem, Incidence matrix, and network optimization complexity.

Explores the knapsack problem and the traveling salesman problem with a focus on optimization algorithms.

Delves into the complexity of factoring polynomials and the implications for security.

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