Name
Abstract | ||
|---|---|---|
Conic optimization is the minimization of a differentiable convex objective function subject to conic constraints. We propose a novel primal–dual first-order method for conic optimization, named proportional–integral projected gradient method (PIPG). PIPG ensures that both the primal–dual gap and the constraint violation converge to zero at the rate of O(1/k), where k is the number of iterations. If the objective function is strongly convex, PIPG improves the convergence rate of the primal–dual gap to O(1/k2). Further, unlike any existing first-order methods, PIPG also improves the convergence rate of the constraint violation to O(1/k3). We demonstrate the application of PIPG in constrained optimal control problems. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.automatica.2022.110359 | Automatica |
Keywords | DocType | Volume |
Convex optimization,First-order methods,Optimal control | Journal | 142 |
ISSN | Citations | PageRank |
0005-1098 | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Yu | 1 | 219 | 29.56 |
| Purnanand Elango | 2 | 1 | 0.35 |
| Ufuk Topcu | 3 | 1 | 0.35 |
| Behçet Açikmese | 4 | 41 | 15.88 |