Multifidelity optimization
Multifidelity optimization combines evaluations of an objective at different levels of accuracy and cost to reduce optimization expense.
Bayesian Optimization in Action
Multifidelity optimization is optimization that combines evaluations of an objective with evaluations of lower-fidelity approximations having different costs and accuracies.
- How it is used
- Balances cheap approximations against costly objective evaluations during Bayesian optimization.
- How deep it goes
- Develops dataset encoding, multifidelity GP modeling, linear costs, ROI acquisition, and budget-based benchmarking; notes nonlinear BoTorch cost models without developing them.
- Examples
- Uses neural-network early stopping, simulations, the Forrester function, and multifidelity Branin approximations.
- What you can do after
- Apply it via BoTorch to fit a multifidelity GP, optimize a cost-aware MES policy, and compare recommendations by budget.
Read how Bayesian Optimization in Action explains multifidelity optimization →
Designing Deep Learning Systems: A guide for software engineers
An optimization approach that uses cheaper approximations of an objective at different fidelity levels to reduce evaluation cost.
- How it is used
- Positions multifidelity optimization as one of three algorithm families for automatic hyperparameter optimization.
- How deep it goes
- Keeps the treatment high level, deferring mathematical reasoning while supplying background for building or configuring an HPO system.
- What you can do after
- Evaluate HPO algorithm families against task constraints and choose an appropriate optimization and deployment approach.
Related concepts
- regularizationuses · Regularization is a modeling technique that constrains or penalizes solutions during fitting or optimization to discourage undesirable complexity or behavior and improve generalization or estimation.
- multifidelity matérn kerneluses · A multifidelity Matérn kernel is a Matérn covariance function extended to model observations from multiple fidelity levels and their correlations.
- surrogate modeluses · A surrogate model is an approximate model that estimates the behavior or outputs of a more complex, costly, or otherwise unavailable model, process, or function.
- budgetuses · A budget is a planned allocation or limit on resources available for an activity, project, or period.
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