## Journals

- Advances in Mathematics of Communications
- Big Data & Information Analytics
- Communications on Pure & Applied Analysis
- Discrete & Continuous Dynamical Systems - A
- Discrete & Continuous Dynamical Systems - B
- Discrete & Continuous Dynamical Systems - S
- Evolution Equations & Control Theory
- Inverse Problems & Imaging
- Journal of Computational Dynamics
- Journal of Dynamics & Games
- Journal of Geometric Mechanics
- Journal of Industrial & Management Optimization
- Journal of Modern Dynamics
- Kinetic & Related Models
- Mathematical Biosciences & Engineering
- Mathematical Control & Related Fields
- Mathematical Foundations of Computing
- Networks & Heterogeneous Media
- Numerical Algebra, Control & Optimization
- Electronic Research Announcements
- Conference Publications
- AIMS Mathematics

NACO

We present a full-step interior-point algorithm for convex quadratic
semi-definite optimization based on a simple univariate function.
The algorithm uses the simple function to determine the search
direction and define the neighborhood of central path. The full-step
used in the algorithm has local quadratic convergence property
according to the proximity function which is also constructed by the
simple function. We derive the iteration complexity for the
algorithm and obtain the best-known iteration bounds for convex
quadratic semi-definite optimization.

JIMO

Based on an equivalent reformulation of the central path, we obtain
a modified-Newton step for linear optimization. Using this step, we
propose an infeasible interior-point algorithm. The algorithm uses
only one full-modified-Newton step search in each iteration. The
complexity bound of the algorithm is the best known for infeasible
interior-point algorithm.

## Year of publication

## Related Authors

## Related Keywords

[Back to Top]