Everything about Conic optimization

🧭 Start here: a 6-step reading path

1. Read the overview: Conic optimizationA 5-minute orientation
2. Skim a recent paper: Real-Time Sequential Conic Optimization for Multi-Phase Rocket Landing GuidanceInfluential work since 2022
3. Pick up a classic book: Shadows of the circleThe most-reprinted book on the subject
4. Read the landmark paper: Linear precoding via conic optimization for fixed MIMO receiversCited 852 times
5. Go sideways: Semidefinite programmingA closely related topic
6. Go sideways: Linear matrix inequalityA closely related topic

🏆 Landmark papers (most cited)

• Linear precoding via conic optimization for fixed MIMO receivers

  A. Wiesel, Y.C. Eldar, S. Shamai · 2006 · IEEE Transactions on Signal Processing · 852 citations

• Tractable Approximations to Robust Conic Optimization Problems

  Dimitris Bertsimas, Melvyn Sim · 2005 · Mathematical Programming · 224 citations

• Facial Reduction Algorithms for Conic Optimization Problems

  Hayato Waki, Masakazu Muramatsu · 2012 · Journal of Optimization Theory and Applications · 54 citations

• A conic scalarization method in multi-objective optimization

  Refail Kasimbeyli · 2011 · Journal of Global Optimization · 53 citations

• The Many Faces of Degeneracy in Conic Optimization

  Dmitriy Drusvyatskiy, Henry Wolkowicz · 2017 · Foundations and Trends® in Optimization · 51 citations

• Towards non-symmetric conic optimization

  Yurii Nesterov · 2012 · Optimization Methods and Software · 45 citations

• Decomposition in Conic Optimization with Partially Separable Structure

  Yifan Sun, Martin S. Andersen, Lieven Vandenberghe · 2014 · SIAM Journal on Optimization · 41 citations

• PICOS: A Python interface to conic optimization solvers

  Guillaume Sagnol, Maximilian Stahlberg · 2022 · Journal of Open Source Software · 33 citations

🆕 Influential since 2022

• Real-Time Sequential Conic Optimization for Multi-Phase Rocket Landing Guidance

  Abhinav G. Kamath, Purnanand Elango, Yue Yu · 2023 · IFAC-PapersOnLine · 38 citations

• Alfonso: Matlab Package for Nonsymmetric Conic Optimization

  Dávid Papp, Sercan Yıldız · 2022 · INFORMS Journal on Computing · 24 citations

• Proportional–integral projected gradient method for conic optimization

  Yue Yu, Purnanand Elango, Ufuk Topcu · 2022 · Automatica · 24 citations

• Mixed-Projection Conic Optimization: A New Paradigm for Modeling Rank Constraints

  Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet · 2022 · Operations Research · 16 citations

• Extrapolated Proportional-Integral Projected Gradient Method for Conic Optimization

  Yue Yu, Purnanand Elango, Behcet Acikmese · 2023 · IEEE Control Systems Letters · 15 citations

📚 Books worth reading

• Shadows of the circleVagn Lundsgaard Hansen · 1998
• 📕Xian xing zhui you huaShu-Cherng Fang · 2013

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

🔗 Related topics

• Semidefinite programming Semidefinite programming (SDP) is a subfield of mathematical programming concerned with the optimization of a linear objective function (a user-specified…
• Linear matrix inequality In convex optimization, a linear matrix inequality (LMI) is an expression of the form LMI ⁡ ( y ) := A 0 + y 1 A 1 + y 2 A 2 + ⋯ + y m A m ⪰ 0 {\displaystyle…
• Duality (optimization) In mathematical optimization theory, duality or the duality principle is the principle that optimization problems may be viewed from either of two perspectives…
• Second-order cone programming A second-order cone program (SOCP) is a convex optimization problem of the form minimize fTx subject to ‖Aix+bi‖2≤ciTx+di,i=1,…,m Fx=g where the problem…
• Quantum optimization algorithms Quantum optimization algorithms are quantum algorithms that are used to solve optimization problems. Mathematical optimization deals with finding the best…
• Convex optimization Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently…
• Ellipsoid method In mathematical optimization, the ellipsoid method is an iterative method for minimizing convex functions over convex sets. The ellipsoid method generates…
• Geometric programming A geometric program (GP) is an optimization problem of the form minimize f 0 ( x ) subject to f i ( x ) ≤ 1 , i = 1 , … , m g i ( x ) = 1 , i = 1 , ……