pyOpt

- Ant Colony Optimization (ACO)
- Population-based stochastic global optimization algorithm based on the behavior of ant colonies, particularly their ability to collectively determine shortest paths through the cumulative affect of pheromones.
- Automatic Differentiation
- A process for evaluating derivatives of a function that depends only on an algorithmic specification of the function, such as a computer program.
- Constraint
- Restriction that a design variables must satisfy, typically denoted in a mathematical program standard form as an inequality, g(x) <= 0, or equality, h(x)=0.
- Genetic algorithm (GA)
- Population-based stochastic global optimization algorithm inspired by the mechanisms of genetics, evolution, and survival of the fittest.
- Global Maximum (or Minimum)
- A feasible solution that maximizes (or minimizes) the value of the objective function over the entire design space feasible region.
- Global Optimizer
- Optimization method that implements an algorithm that is designed to find a globally optimal solution for various kinds of nonconvex programming problems.
- Local Maximum (or Minimum)
- A feasible solution that maximizes (or minimizes) the value of the objective function within a local neighborhood of that solution.
- Lower Bound
- A constraint that specifies a minimum feasible value of an individual design variable.
- Numerical Optimization
- Mathematical techniques and procedures used to make a system or design as effective and/or functional as possible
- Objective Function
- The (real-valued) function to be optimized, typically denoted in a mathematical program standard form as f.
- Particle Swarm Optimization (PSO)
- Population-based stochastic global optimization algorithm based on the optimal swarm behavior of animals, like bird flocking and bees.
- Sequential Linear Programming (SLP)
- Gradient-based local optimization algorithm based on solving successive first order approximations of a nonlinear programming problem objective subject to a linearization of the constraints. The linear approximations are usually done by using the first-order Taylor expansion.
- Sequential Quadratic Programming (SQP)
- Gradient-based local optimization algorithm based on solving successive second order approximations of a nonlinear programming problem objective subject to a linearization of the constraints. The approximations are usually done by using the second-order Taylor expansion.
- Sequential Unconstrained Minimization Technique (SUMT)
- Convex programming algorithm that convert the original constrained optimization problem to a sequence of unconstrained optimization problems whose optimal solutions converge to an optimal solution for the original problem.
- Upper Bound
- A constraint that specifies a maximum feasible value of an individual design variable.