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