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Optimization Techniques
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Genetic Algorithms
Simulate natural evolutionary processes to find optimal or near-optimal solutions
Quadratic Programming (QP)
Interior-point methods or Active-set methods
Integer Programming (IP)
Branch and Bound, Cutting Planes, or Integer Simplex
Stochastic Gradient Descent
Variation of gradient descent where only a subset (minibatch) of the dataset is used to compute the gradient at each step
Ant Colony Optimization
Probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs
Unconstrained Optimization
Steepest descent or Newton's method
Global Optimization
Methods to find the global solution, not just local optima, such as Basin-hopping or Differential evolution
Linear Programming (LP)
Simplex algorithm or Interior-point methods
Multi-Objective Optimization
Pareto efficiency, Weighted sum approach, or Goal programming
Constrained Optimization
Lagrange multipliers, KKT conditions, or Penalty methods
Evolutionary Strategies
Optimization algorithms inspired by biological evolution, such as Covariance Matrix Adaptation Evolution Strategy (CMA-ES)
Heuristic Methods
Problem-specific approaches like Hill-climbing, Tabu search, or Greedy algorithms
Simulated Annealing
Probabilistic technique for approximating the global optimum of a given function
Nonlinear Programming
Gradient descent, Newton's method, or Sequential quadratic programming (SQP)
Dynamic Programming
Breaking down problems into simpler subproblems and solving recursively or iteratively
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