A C++ interface to formulate and solve linear, quadratic and second order cone problems.
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Updated
Jul 30, 2021 - C++
A C++ interface to formulate and solve linear, quadratic and second order cone problems.
A C++ Second Order Cone Solver based on Eigen
Simulation code for "A max-min task offloading algorithm for mobile edge computing using non-orthogonal multiple access," by V. Kumar, M. F. Hanif, M. Juntti and L. -N. Tran, published in IEEE Transactions on Vehicular Technology, vol. 72, no. 9, pp. 12332-12337, Sept. 2023, doi: 10.1109/TVT.2023.3263791.
Examples demonstrating the nAG Library for Java
Powered-descent guidance in dependency-free C++17: a from-scratch interior-point SOCP solver driving lossless convexification (3-DoF) and 6-DoF successive convexification, with a rigid-body sim and Monte Carlo dispersion.
火星着陆 SOCP 轨迹优化 — 基于 ECOS 二阶锥规划求解器,Intel N150 优化
Robust Convex Optimization for Home Energy Management — LP/SOCP with Pakistan TOU Tariffs, Load Shedding, and Interactive Dashboard
Fuel-optimal powered-descent guidance (G-FOLD): Acikmese-Blackmore lossless-convexification of Falcon-9-style rocket landing as a second-order-cone program. ~22% less fuel vs baseline, 100% feasible, ~2m landing accuracy, ~9ms solves. Python, cvxpy/ECOS.
SpaceX Falcon 9 first-stage vertical propulsive landing simulation using G-FOLD convex optimization (SOCP). 100% Monte Carlo success rate across 100 runs! Features: 6-DOF dynamics, multi-engine 1-3-1 trajectory profile, MEKF state estimation, CLARABEL solver, fault injection hardening. Python + C++ | 猎鹰九号一级垂直回收G-FOLD凸优化仿真,蒙特卡洛100%成功率
SCvx 3-DoF powered-descent trajectory solver — flight-grade Rust (no_std, bounded-WCET, C-FFI)
Research toolbox for polynomial optimization using Sum-of-Squares (SOS) and moment relaxations (SDP, SOCP, chordal methods)
Computational Trajectory Generation with hand-parser based on Sequential Convex Programming
Convex relaxation methods for QCQPs, including SDP, McCormick, CCD, and polynomial reformulation for SOS workflows.
Diabetes classification on the Pima dataset using nominal SVM and robust SOCP models, with six imputation methods and synthetic measurement-noise stress testing.
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