Dynamic Programming: How Many Ways to Make Change

Related lectures (30)

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Explores the integrality gap concept in set cover and multiplicative weights algorithms.

Explores the ingredients and selection of algorithms for different goals.

Explores energy systems modeling, optimization, and cost analysis for efficient operations.

Covers semi-definite programming and optimization over positive semidefinite cones.

Explores solving strategies for energy system optimization problems and different types of optimization approaches.

Explores controlled stochastic processes, focusing on analysis, behavior, and optimization, using dynamic programming to solve real-world problems.

Explores the transition from algorithms to programs through compilation, emphasizing constraints and machine-understandable coding practices.

Explores challenges in minimizing job completion time in distributed computing, focusing on data skew impact and efficient processing.

Explores the modeling of the '100 prisoners' problem and compares naive and optimal approaches.

Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.