#optimization
10 concepts
Concepts
Optimization Basics
Optimization is finding variable values that maximize or minimize an objective function under given constraints.
Gradient Descent
Gradient descent is an optimization algorithm that iteratively moves in the opposite direction of the gradient to find the minimum.
Convex Optimization
Convex optimization minimizes a convex function over a convex set. Local minima are global minima, making it efficiently solvable.
Gradient Descent
An optimization algorithm that iteratively moves in the opposite direction of the gradient to find the minimum.
Linear Programming
A mathematical method to optimize a linear objective function subject to linear constraints.
Linear Programming
Method for optimizing linear objective function subject to linear constraints
Dynamic Programming (OR)
Decomposing complex problems into subproblems for sequential optimization
Conjugate Gradient Method
An efficient iterative method for solving systems with positive definite symmetric matrices. Equivalent to minimizing a quadratic form, widely used in optimization.
Network Flow
The problem of finding maximum flow from source to sink in a directed graph. The max-flow min-cut theorem is the key result.
Matching Theory
The problem of finding a set of edges with no shared vertices. The existence of a perfect matching in bipartite graphs is given by Hall's theorem.