TinySDP is the first semidefinite programming solver designed for embedded systems, enabling real-time certifiable model predictive control with nonconvex geometric constraints on microcontrollers.
Semidefinite programming.SIAM review, 38(1):49–95
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For generically generated moment matrices with O(n^d) atoms, the minimal face of the pseudo-moment cone is simplicial, enabling an efficient Carathéodory-type atomic decomposition algorithm.
An SDP relaxation combined with a rounding scheme solves the balanced minimum evolution phylogenetic inference problem and produces accurate trees on simulated and empirical data.
citing papers explorer
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TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics
TinySDP is the first semidefinite programming solver designed for embedded systems, enabling real-time certifiable model predictive control with nonconvex geometric constraints on microcontrollers.
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Simplicial Regularizability of the Pseudo-Moment Cone and Carath\'eodory-Type Atomic Decomposition of Moment Matrices
For generically generated moment matrices with O(n^d) atoms, the minimal face of the pseudo-moment cone is simplicial, enabling an efficient Carathéodory-type atomic decomposition algorithm.
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Phylogenetic Inference under the Balanced Minimum Evolution Criterion via Semidefinite Programming
An SDP relaxation combined with a rounding scheme solves the balanced minimum evolution phylogenetic inference problem and produces accurate trees on simulated and empirical data.