modOpt
0.2.1

Contents

  • Getting Started
  • Defining Optimization Problems
    • ProblemLite
    • Problem
    • CSDL_alpha
    • OpenMDAO
    • Jax
    • CasADi
    • CSDL (deprecated)
  • Solving Optimization Problems
    • Performant algorithms
      • SLSQP
      • PySLSQP
      • COBYLA
      • BFGS
      • LBFGSB
      • NelderMead
      • COBYQA
      • TrustConstr
      • OpenSQP
      • SNOPT
      • IPOPT
      • ConvexQPSolvers
      • CVXOPT
    • Educational algorithms
      • SteepestDescent
      • Newton
      • QuasiNewton
      • NewtonLagrange
      • L2PenaltyEq
      • SQP
      • InteriorPoint
      • NelderMeadSimplex
      • PSO
      • SimulatedAnnealing
  • Post-processing
  • New Optimizer Development
  • Benchmarking
    • CUTEst Problem Table
  • Developer Docs
    • Problem
    • ProblemLite
    • Optimizer
  • Tutorials
    • A simple example (unconstrained)
  • Examples
    • Basic examples
      • 1. Minimizing a Quartic function using the ProblemLite class
      • 2. Minimizing a Quartic function
      • 3. Minimizing the Rosenbrock function
      • 4. Minimizing a Quartic function with constraints
      • 5. Minimizing a Quartic function with constraints and problem scaling
      • 6. Quartic optimization with separate constraints
      • 7. Optimization with CSDL models
      • 8. Optimization with CSDL_alpha models
      • 9. Quartic optimization using CasADi
      • 10. Quartic optimization using Jax
      • 11. Optimization with OpenMDAO models
      • 12. Method of Newton Lagrange
      • 13. Minimizing the Bean function
    • Advanced examples
      • 1. Traveling Salesman Problem
      • 2. Benchmark optimization algorithms on four simple problems
      • 3. Benchmark instructional algorithms on three analytical problems
      • 4. Benchmark performant algorithms on three analytical problems
      • 5. Benchmark algorithms using the uncoupled Rosenbrock problem
      • 6. Benchmark algorithms using the coupled Rosenbrock problem
      • 7. Cantilever beam optimization with finite difference gradients
      • 8. Cantilever beam optimization with CasADi
      • 9. Cantilever beam optimization with CSDL
      • 10. Cantilever beam optimization with Jax
      • 11. Cantilever beam optimization with OpenMDAO
      • 12. Starship 2D trajectory optimization with finite difference gradients
      • 13. Starship 2D trajectory optimization with CasADi
      • 14. Starship 2D trajectory optimization with CSDL
      • 15. Starship 2D trajectory optimization with Jax
      • 16. Starship 2D trajectory optimization with OpenMDAO
      • 17. Import and solve a CUTEst problem using modOpt
      • 18. Benchmark OpenSQP against other solvers on CUTEst problems (nx,nc<=100)
  • API Reference
    • modopt.Optimizer
    • modopt.optimize
    • modopt.Problem
    • modopt.ProblemLite
    • modopt.JaxProblem
    • modopt.CasadiProblem
    • modopt.CSDLAlphaProblem
    • modopt.OpenMDAOProblem
    • modopt.CSDLProblem (deprecated)
    • modopt.postprocessing
    • Educational algorithms
    • Performant algorithms
    • modopt.line_search_algorithms
    • modopt.merit_functions
    • modopt.approximate_hessians
    • modopt.ConvexQPSolvers
    • modopt.CUTEstProblem
    • modopt.benchmarking
  • Contributing to modOpt
  • Changelog
  • License

Performant algorithms

  • SLSQP
  • PySLSQP
  • COBYLA
  • BFGS
  • LBFGSB
  • NelderMead
  • COBYQA
  • TrustConstr
  • OpenSQP
  • SNOPT
  • IPOPT
  • ConvexQPSolvers
  • CVXOPT

Educational algorithms

  • SteepestDescent
  • Newton
  • QuasiNewton
  • NewtonLagrange
  • L2PenaltyEq
  • SQP
  • InteriorPoint
  • NelderMeadSimplex
  • PSO
  • SimulatedAnnealing
modOpt
  • Search

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