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Most codes are based on [[interior point methods]] (CSDP, [[MOSEK]], SeDuMi, [https://www.math.cmu.edu/~reha/sdpt3.html SDPT3], DSDP, SDPA). RobustThese are robust and efficient for general linear SDP problems., but Restrictedrestricted by the fact that the algorithms are second-order methods and need to store and factorize a large (and often dense) matrix. Theoretically, the state-of-the-art high-accuracy SDP algorithms<ref>{{Cite journal |last1=Jiang |first1=Haotian |last2=Kathuria |first2=Tarun |last3=Lee |first3=Yin Tat |last4=Padmanabhan |first4=Swati |last5=Song |first5=Zhao |date=November 2020 |title=A Faster Interior Point Method for Semidefinite Programming |url=https://ieeexplore.ieee.org/document/9317892 |journal=2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS) |___location=Durham, NC, USA |publisher=IEEE |pages=910–918 |doi=10.1109/FOCS46700.2020.00089 |arxiv=2009.10217 |isbn=978-1-7281-9621-3|s2cid=221836388 }}</ref><ref>{{Cite arXiv |last1=Huang |first1=Baihe |last2=Jiang |first2=Shunhua |last3=Song |first3=Zhao |last4=Tao |first4=Runzhou |last5=Zhang |first5=Ruizhe |date=2021-11-18 |title=Solving SDP Faster: A Robust IPM Framework and Efficient Implementation |class=math.OC |eprint=2101.08208}}</ref> are based on this approach.
