Learn to optimize-a brief overview.

Area of Science:

• Computational Science
• Machine Learning
• Algorithm Optimization

Background:

• Practical optimization problems often require highly configurable algorithms.
• Tuning these algorithms via trial-and-error is computationally expensive, especially for machine learning tasks.
• Existing research focuses on accelerating this configuration process through learning from training data.

Purpose of the Study:

• To review and summarize the field of
• learn to optimize
• methods.

Main Methods:

• Literature review of studies employing machine learning to optimize algorithm configuration.
• Categorization and analysis of different approaches within the
• learn to optimize
• paradigm.

Main Results:

• The field of
• learn to optimize
• has seen significant progress in accelerating algorithm configuration.
• Various techniques have been developed to learn optimal parameters from training instances.

Conclusions:

• Learning to optimize offers a promising direction to reduce the computational cost of configuring complex algorithms.
• Further research in this area can lead to more efficient solutions for computationally intensive optimization problems.