Live briefing
Everything about Semidefinite programming
Subfield of convex optimization
Semidefinite programming (SDP) is a subfield of mathematical programming concerned with the optimization of a linear objective function over the intersection of the cone of positive semidefinite matrices with an affine space, i.e., a spectrahedron. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Semidefinite programmingA 5-minute orientation
- Skim a recent paper: Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite ProgrammingInfluential work since 2022
- Pick up a classic book: Approximation algorithms and semidefinite programmingThe most-reprinted book on the subject
- Read the landmark paper: Semidefinite ProgrammingCited 3,379 times
- Go sideways: Quantum optimization algorithmsA closely related topic
- Go sideways: Schmidt decompositionA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Semidefinite Programming
Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
SDPT3 — A Matlab software package for semidefinite programming, Version 1.3
Complex Quadratic Optimization and Semidefinite Programming
Strong Duality for Semidefinite Programming
Robust Control via Sequential Semidefinite Programming
Complementarity and nondegeneracy in semidefinite programming
Regularization Methods for Semidefinite Programming
🆕 Influential since 2022
Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programming
Semidefinite programming relaxations for quantum correlations
Noisy intermediate-scale quantum algorithm for semidefinite programming
Semidefinite Programming for Wireless Cooperative Localization Using Biased RSS Measurements
Quantum key distribution rates from semidefinite programming
📚 Books worth reading
Approximation algorithms and semidefinite programmingBernd Gärtner · 2012
Semidefinite Optimization And Convex Algebraic GeometryGrigoriy Blekherman · 2012- 📕Polyhedral and semidefinite programming methods in combinatorial optimizationLevent Tuncel · 2010
- 📕Polyhedral and semidefinite programming methods in combinatorial optimizationLevent Tuncel · 2010
- 📕Sum of SquaresPablo A. Parrilo, Rekha R. Thomas · 2020
Invitation to Nonlinear AlgebraMateusz Michaek, Bernd Sturmfels · 2021
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Quantum optimization algorithms Quantum optimization algorithms are quantum algorithms that are used to solve optimization problems. Mathematical optimization deals with finding the best…
- Schmidt decomposition In linear algebra, the Schmidt decomposition (named after its originator Erhard Schmidt) refers to a particular way of expressing a vector in the tensor…
- Positive operator In mathematics (specifically linear algebra, operator theory, and functional analysis) as well as physics, a linear operator A {\displaystyle A} acting…
- Superoperator In physics, a superoperator is a linear operator acting on a vector space of linear operators. Sometimes the term refers more specially to a completely…
- Quantum tomography Quantum tomography or quantum state tomography is the process by which a quantum state is reconstructed using measurements on an ensemble of identical…
- Kaczmarz method The Kaczmarz method or Kaczmarz's algorithm is an iterative algorithm for solving linear equation systems A x = b {\displaystyle Ax=b} . It was first discovered…
- Householder transformation In linear algebra, a Householder transformation (also known as a Householder reflection or elementary reflector) is a linear transformation that describes…
- Separable state In quantum mechanics, separable states are multipartite quantum states that can be written as a convex combination of product states. Product states are…