Branch and bound

Formal sciences
Theoretical computer science
Algorithms and data structures
Combinatorial optimization

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Related lectures (32)

Formulation, Properties: Infimum vs Optimum

Explains the difference between infimum and optimum in optimization problems and how to find the optimal solution.

Dynamical System Theory for Engineers: Large Scale Stability

Covers the concept of large scale stability in dynamical systems.

Network Flows with Capacities: Revisited

Revisits network flows with capacities, focusing on bounded flow problems and different formulations to approach them.

Rescuing Data Center Processors

Explores challenges and solutions for data center processors, focusing on efficiency, cache issues, branch prediction, and architectural optimizations.

Approximation Algorithms

Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.

Graph Theory and Network Flows

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Search Algorithms: Abductive Reasoning

Covers search algorithms, focusing on abductive reasoning and heuristic search strategies.

Discrete Optimization: Relaxation

Explores solving discrete optimization problems by relaxing integrality constraints.

Search Algorithms: Abductive Reasoning

Explores abductive reasoning, search algorithms, and heuristic search for problem-solving.

Theory of Computation: Monotone Complexity and XOR-SAT Lower Bounds

Explores monotone complexity, XOR-SAT lower bounds, and their implications in computational theory.

Digital Image Correlation

Covers digital image correlation, similarity criteria, iterative approaches, and practical applications in 2D and 3D correlation.

Optimization Techniques: Gradient Descent and Convex Functions

Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.

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