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Advanced Optimization Algorithms Research

Advanced Optimization Algorithms Research is a research topic within Numerical Analysis. Science Explorer counts 30k research works in it since 1950. 17.1% of them reached the world's top 10% most cited for their field and year.

This cluster of papers covers advances in numerical optimization techniques, including topics such as benchmarking optimization software, semidefinite programming, global optimization, derivative-free optimization, and the application of sum of squares techniques. It also explores interior-point methods, quadratic programming, and mixed-integer nonlinear programs.

  • Optimization Software
  • Semidefinite Programming
  • Global Optimization
  • Nonlinear Programming
  • Interior-Point Methods
  • Derivative-Free Optimization
  • Quadratic Programming
  • Convex Optimization
  • Mixed-Integer Nonlinear Programs
  • Sum of Squares Techniques
Research works
30k
fractional, since 1950
In the world top 10%
5k
per year above
Top-10% rate
17.1%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+1%
the tick is no change

Which countries lead Advanced Optimization Algorithms Research research?

By volume, China and the United States publish the most (833 and 495 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 833 works
  2. 2 United States 495 works
  3. 3 India 259 works
  4. 4 Germany 169 works
  5. 5 France 129 works
  6. 6 Russia 114 works
  7. 7 Iran 97 works
  8. 8 Vietnam 95 works
  9. 9 Italy 95 works
  10. 10 Japan 83 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 35.2%United States: 20.9%India: 10.9%Germany: 7.1%6 others listed: 25.9%35%largest
China833 · 35.2%United States495 · 20.9%India259 · 10.9%Germany169 · 7.1%6 others listed614 · 25.9%

Shares of the rows listed above, not of the whole node.

Which institutions lead Advanced Optimization Algorithms Research research?

By volume in 2022–2025, Technion – Israel Institute of Technology publishes the most Advanced Optimization Algorithms Research research, followed by Georgia Institute of Technology and University of Mosul.

Who are the leading researchers in Advanced Optimization Algorithms Research?

The most-cited researchers publishing on Advanced Optimization Algorithms Research include Stanley Osher, Kalyanmoy Deb and Michael I. Jordan.

  1. 1 Stanley Osher United States 8k citations
  2. 2 Kalyanmoy Deb United States 6.7k citations
  3. 3 Michael I. Jordan United States 4.8k citations
  4. 4 Zhu Han United States 4.7k citations
  5. 5 A. Charnes United States 4.5k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Advanced Optimization Algorithms Research research done?

The largest centres of Advanced Optimization Algorithms Research research in 2022–2025 are Beijing (China), Shanghai (China), Chongqing (China) and Moscow (Russia). Among places with at least 20 works in it, it is an unusually large share of all research in Haifa.

Largest cities, 2022–2025

  1. 1 Beijing China 100 works
  2. 2 Shanghai China 66 works
  3. 3 Chongqing China 45 works
  4. 4 Moscow Russia 42 works
  5. 5 Nanjing China 40 works
  6. 6 Paris France 38 works
  7. 7 Hanoi Vietnam 38 works
  8. 8 Tokyo Japan 36 works
  9. 9 Bucharest Romania 36 works
  10. 10 Xi'an China 35 works

Where it is the local speciality

  1. HaifaIL · 33.3 works12×
← less than its size predictsmore →

Location quotient: how much more of its research is in Advanced Optimization Algorithms Research than the world average.

See Advanced Optimization Algorithms Research on the map

Where is the best place to study Advanced Optimization Algorithms Research?

Among universities, judged by research, China Medical University, University of KwaZulu-Natal and Universitat Politècnica de València score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 20.59%fractional works in this node (log) →share in the world top 10% →China Medical University: 10, 26.4%University of KwaZulu-Natal: 15, 22.3%Universitat Politècnica de València: 17, 25.8%Cameron University: 17, 10.2%Guangxi University: 17, 35.8%Georgia Institute of Technology: 19, 18.2%Bayero University Kano: 8, 17.9%Technion – Israel Institute of Technology: 30, 5.1%Zhejiang Normal University: 13, 22.0%National Institute of Technology Karnataka: 10, 22.2%China Medical Univer…Universitat Politècn…University of KwaZul…Cameron University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 China Medical UniversityTaiwan 73.226.4%20.2×10 +1084.7%
2 University of KwaZulu-NatalSouth Africa 72.422.3%11.4×15 +1335.0%
3 Universitat Politècnica de ValènciaSpain 67.225.8%12.6×17 +44.0%
4 Cameron UniversityUnited States 62.310.2%880.3×17 +179.1%
5 Guangxi UniversityChina 61.935.8%7.7×17 -36.7%
6 Georgia Institute of TechnologyUnited States 59.518.2%8.4×19 +82.4%
7 Bayero University KanoNigeria 59.317.9%24.6×8 +199.7%
8 Technion – Israel Institute of TechnologyIsrael 57.65.1%24.7×30 +25.0%
9 Zhejiang Normal UniversityChina 53.922.0%12.5×13 -44.5%
10 National Institute of Technology KarnatakaIndia 53.822.2%15.2×10

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Advanced Optimization Algorithms Research research growing?

Output in 2018–2022 was 1% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Advanced Optimization Algorithms Research.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.