Live briefing
Everything about Global optimization
Branch of mathematics
Global optimization is a branch of operations research, applied mathematics, and numerical analysis that attempts to find the global minimum or maximum of a function or a set of functions on a given set. It is usually described as a minimization problem because the maximization of the real-valued function is equivalent to the minimization of the function. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Global optimizationA 5-minute orientation
- Skim a recent paper: Dung beetle optimizer: a new meta-heuristic algorithm for global optimizationInfluential work since 2022
- Pick up a classic book: Models, Algorithms and Technologies for Network AnalysisThe most-reprinted book on the subject
- Read the landmark paper: Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous SpacesCited 25,917 times
- Go sideways: Stochastic tunnelingA closely related topic
- Go sideways: Stochastic optimizationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
Efficient Global Optimization of Expensive Black-Box Functions
Global Portfolio Optimization
Chaotic grasshopper optimization algorithm for global optimization
Diffusions for Global Optimization
Thermodynamics of Global Optimization
Global optimization
Chaos Enhanced Bacterial Foraging Optimization for Global Optimization
🆕 Influential since 2022
Dung beetle optimizer: a new meta-heuristic algorithm for global optimization
American zebra optimization algorithm for global optimization problems
Attraction–Repulsion Optimization Algorithm for Global Optimization Problems
A Hybrid Moth Flame Optimization Algorithm for Global Optimization
Modified Remora Optimization Algorithm with Multistrategies for Global Optimization Problem
📚 Books worth reading
Models, Algorithms and Technologies for Network AnalysisMikhail V. Batsyn, Valery A. Kalyagin · 2012
Riemannian geometry and geometric analysisJürgen Jost · 1995
Optimism over despairNoam Chomsky, Chronis Polychroniou · 2017- 📕Applied Functional AnalysisEberhard Zeidler · 1995
Nonlinear AnalysisQamrul Hasan Ansari · 2014
The stability of matterElliott H. Lieb · 1991
Implicit Functions and Solution MappingsAsen L. Dontchev, R. Tyrrell Rockafellar · 2009
Applied functional analysisEberhard Zeidler · 1995
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Stochastic tunneling In numerical analysis, stochastic tunneling (STUN) is an approach to global optimization based on the Monte Carlo method-sampling of the function to be…
- Stochastic optimization Stochastic optimization (SO) are optimization methods that generate and use random variables. For stochastic optimization problems, the objective functions…
- Mathematical optimization Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criteria…
- Variational Monte Carlo In computational physics, variational Monte Carlo (VMC) is a quantum Monte Carlo method that applies the variational method to approximate the ground state…
- List of numerical analysis topics This is a list of numerical analysis topics. Validated numerics Iterative method Rate of convergence — the speed at which a convergent sequence approaches…
- Cross-entropy method The cross-entropy (CE) method is a Monte Carlo method for importance sampling and optimization. It is applicable to both combinatorial and continuous problems…
- Simulated annealing Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to…
- Multi-objective optimization In multiple-criteria decision making, multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization…