Second-Order Cone Programming

Also known as SOCP

Follow this topic Ask about this topic

Latest papers

9 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Learning SOCP Solver Dynamics with Graph Neural Networks
Oct 19, 2026Adam Ghalem, Lou Salaün, Davide Rinaldi +1Second-Order Cone Programming Learning to Optimize
Université Paris-Saclay, CentraleSupélec, Inria · Nokia Bell Labs
Certified Robustness via Sparse Second-Order Cone Relaxations
Oct 12, 2026Mohammadreza Maleki, Daniel Sadig, Rushendra Sidibomma +3Convex Relaxation Second-Order Cone Programming
Toronto Metropolitan University · University of Minnesota Twin Cities · Johns Hopkins University +2
Dual-Arm Box Manipulation via Online Inertial Estimation and Convex Wrench Optimization
May 21, 2026Kenzhi Iskandar Wong, Lin Yang, Qian Ying Lee +1Robotic Grasping Bimanual Robotic Manipulation
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
A cell-decomposition based path planner for 3D navigation in constrained workspaces
May 11, 2026João P. L. Morais, Luciano C. A. Pimenta, Marcelo A. Santos +1Second-Order Cone Programming Combinatorial Optimization
Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil · Department of Management, Information and Production Engineering, University of Bergamo, Dalmine, BG, Italy · Department of Electronic Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
Exact Dual Geometry of SOC-ICNN Value Functions
May 6, 2026Kang Liu, Jianchen Hu, Wei PengSecond-Order Cone Programming Neural Network Optimization
School of Future Technology Xi’an Jiaotong University Xi’an, China · School of Future Technology School of Automation Science and Engineering Xi’an Jiaotong University Xi’an, China · School of Automation Science and Engineering Xi’an Jiaotong University Xi’an, China
SOC-ICNN: From Polyhedral to Conic Geometry for Learning Convex Surrogate Functions
Apr 24, 2026Kang Liu, Jianchen Hu, Wei PengNeural Surrogate Modeling Second-Order Cone Programming
School of Future Technology Xi’an Jiaotong University Xi’an, China · School of Future Technology School of Automation Science and Engineering Xi’an Jiaotong University Xi’an, China · School of Automation Science and Engineering Xi’an Jiaotong University Xi’an, China
TacDexGrasp: Compliant and Robust Dexterous Grasping with Tactile Feedback
Mar 7, 2026Yubin Ke, Jiayi Chen, Hang Lv +2Second-Order Cone Programming Robotic Grasping
Peking University · Galbot · Beijing Academy of Artificial Intelligence
cuNRTO: GPU-Accelerated Nonlinear Robust Trajectory Optimization
Mar 3, 2026Jiawei Wang, Arshiya Taj Abdul, Evangelos A. TheodorouSecond-Order Cone Programming Trajectory Optimization
University of California, San Diego · Georgia Institute of Technology, Atlanta
MeshCone: Second-Order Cone Programming for Geometrically-Constrained Mesh Enhancement
Dec 11, 2024Alexander ValverdeSecond-Order Cone Programming 3D Mesh Deformation
University of California, Santa Cruz

Oct 19, 2026J/K move · Enter open · S save

Learning SOCP Solver Dynamics with Graph Neural Networks

Adam Ghalem, Lou Salaün, Davide Rinaldi, Fragkiskos D. Malliaros

Université Paris-Saclay, CentraleSupélec, Inria · Nokia Bell Labs

Second-Order Cone Programming Learning to Optimize Constrained Optimization Graph Neural Networks

Open paper AskPDF arXiv

Second-order cone programs (SOCPs) are a fundamental class of convex optimization problems widely applied across engineering domains. Classical interior-point methods are robust and accurate, but their iterative nature can be costly in real-time or repeated-solve settings. This has motivated learning-based solvers that approximate optimizer solutions with neural networks. Recent work has increasingly studied graph neural networks (GNNs) for linear, quadratic, and second-order cone optimization, leveraging their ability to model sparse relational structure among variables, constraints, and objective functions. Most existing GNN-based solvers compress the optimization process into a single forward pass, predicting the optimizer directly without using the intermediate trajectory produced by classical solvers. We introduce SOCPNet, a recursive GNN framework that uses solver trajectories to learn an iterative SOCP solution procedure. A shared GNN model is applied repeatedly, refining the current iterate at each step and enabling inference for a variable number of iterations. Rather than predicting the solution in a single pass, SOCPNet learns from intermediate iterates to progressively refine its estimates using a trajectory-regularized training procedure. We evaluate SOCPNet on multiple datasets spanning standard SOCP problem classes and show that it consistently reduces the final solution error relative to a SOCP-GNN baseline. Finally, we demonstrate how trained SOCPNet models can be used in a hybrid optimization pipeline by warm-starting a classical interior-point solver, reducing solver iterations without loss of accuracy.