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Everything about Deterministic global optimization

Branch of numerical optimization

Deterministic global optimization is a branch of mathematical optimization which focuses on finding the global solutions of an optimization problem whilst providing theoretical guarantees that the reported solution is indeed the global one, within some predefined tolerance. The term "deterministic global optimization" typically refers to complete or rigorous optimization methods. Rigorous methods converge to the global optimum in finite time. Deterministic global optimization methods are typically used when locating the global solution is a necessity, when it is extremely difficult to find a feasible solution, or simply when the user desires to locate the best possible solution to a problem. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Deterministic global optimizationA 5-minute orientation
  2. Skim a recent paper: Convex and concave envelopes of artificial neural network activation functions for deterministic global optimizationInfluential work since 2022
  3. Pick up a classic book: Global optimizationThe most-reprinted book on the subject
  4. Read the landmark paper: Deterministic Global Optimization with Artificial Neural Networks EmbeddedCited 202 times
  5. Go sideways: ANTIGONEA closely related topic
  6. Go sideways: Artelys KnitroA closely related topic

📖 Definitions

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🗂️ The subject at a glance

  1. Overview
  2. Classes of deterministic global optimization problems
  3. Zero-order methods
  4. First-order methods
  5. Second-order methods
  6. Deterministic global optimization solvers

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics