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Found 8 papers, 6 papers with code
Control of Cross-Directional Systems with Approximate Symmetries
We show that our proposed approximations can yield stable systems even when the Frobenius norm approximation does not.
The Past Does Matter: Correlation of Subsequent States in Trajectory Predictions of Gaussian Process Models
Computing the distribution of trajectories from a Gaussian Process model of a dynamical system is an important challenge in utilizing such models.
Learning ODE Models with Qualitative Structure Using Gaussian Processes
Recent advances in learning techniques have enabled the modelling of dynamical systems for scientific and engineering applications directly from data.
COSMO: A conic operator splitting method for convex conic problems
This paper describes the Conic Operator Splitting Method (COSMO) solver, an operator splitting algorithm for convex optimisation problems with quadratic objective function and conic constraints.
Optimization and Control
OSQP: An Operator Splitting Solver for Quadratic Programs
We present a general purpose solver for convex quadratic programs based on the alternating direction method of multipliers, employing a novel operator splitting technique that requires the solution of a quasi-definite linear system with the same coefficient matrix at almost every iteration.
Optimization and Control
Chordal decomposition in operator-splitting methods for sparse semidefinite programs
We employ chordal decomposition to reformulate a large and sparse semidefinite program (SDP), either in primal or dual standard form, into an equivalent SDP with smaller positive semidefinite (PSD) constraints.
Optimization and Control
Fast ADMM for homogeneous self-dual embedding of sparse SDPs
We propose an efficient first-order method, based on the alternating direction method of multipliers (ADMM), to solve the homogeneous self-dual embedding problem for a primal-dual pair of semidefinite programs (SDPs) with chordal sparsity.
Optimization and Control
Fast ADMM for Semidefinite Programs with Chordal Sparsity
We show that chordal decomposition can be applied to either the primal or the dual standard form of a sparse SDP, resulting in scaled versions of ADMM algorithms with the same computational cost.
Optimization and Control
