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optimagic
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  • Tutorials
    • Numerical optimization
    • Numerical differentiation
    • bayes_opt Optimizer in optimagic
  • How-to Guides
    • How to write objective functions
    • How to specify params
    • How to speed up your optimization using derivatives
    • How to specify and configure algorithms
    • How to select a local optimizer
    • How to specify bounds
    • How to specify constraints
    • How to choose a strategy for global optimization
    • How to do multistart optimizations
    • How to visualize optimizer histories
    • How to scale optimization problems
    • How to use logging
    • How to handle errors during optimization
    • How to visualize an optimization problem
    • How to Benchmark Optimization Algorithms
    • How to add optimizers to optimagic
    • How to document optimizers
  • Explanation
    • How constraints are implemented
    • Internal optimizers for optimagic
    • Why optimization is difficult
    • Introduction to basic types of numerical optimization algorithms
    • How supported optimization algorithms are tested
    • Numerical differentiation: methods
  • optimagic API
    • Utility functions
    • The default algorithm options
    • Batch evaluators
    • Types
  • Development
    • Code of Conduct
    • How to contribute
    • Styleguide
    • Enhancement Proposals
      • EP-00: Governance model & code of conduct
      • EP-01: Pytrees
      • EP-02: Static typing
      • EP-03: Alignment with SciPy
    • Credits
    • Changes
  • Videos
  • Optimizers
  • Estimagic
    • Estimagic Tutorials
      • Likelihood estimation
      • Method of Simulated Moments (MSM)
      • Bootstrap Tutorial
      • How to generate publication quality tables
    • Explanation
      • Bootstrap Confidence Intervals
      • Bootstrap Monte Carlo Comparison
      • Robust Likelihood inference
    • estimagic API
  • Installation
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  • Bootstrap Confidence Intervals
  • Bootstrap Monte Carlo Comparison
  • Robust Likelihood inference
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