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Covers the Branch & Bound algorithm for efficient exploration of feasible solutions and discusses LP relaxation, portfolio optimization, Nonlinear Programming, and various optimization problems.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Explores the Branch and Bound algorithm in discrete optimization, efficiently finding optimal solutions by calculating lower bounds on subsets.
Covers the concept of hedging for Linear Programs and the simplex method, focusing on minimizing costs and finding optimal solutions.
Explores optimization methods in engineering, covering decision variables, constraints, and various solving techniques.
Covers methods for solving nonlinear optimization problems, including direct search, Newton-Raphson, and branch and bound.
