Cyclic seesaw process for optimization and identification
From MaRDI portal
Publication:1762401
DOI10.1007/s10957-012-0001-1OpenAlexW2063735865MaRDI QIDQ1762401
Publication date: 26 November 2012
Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10957-012-0001-1
system identificationparameter estimationrecursive estimationalternating optimizationnondifferentiableblock coordinate optimizationcyclic optimization
Related Items (4)
Unnamed Item ⋮ Analysis of practical step size selection in stochastic approximation algorithms ⋮ Predictive Algorithm for Detection of Microcracks from Macroscale Observables ⋮ Cyclic stochastic approximation with disturbance on input in the parameter tracking problem based on a multiagent algorithm
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- On simultaneous optimization of truss geometry and topology
- Optimization. Algorithms and consistent approximations
- Pooling Problem: Alternate Formulations and Solution Methods
- A new method for evaluating the log-likelihood gradient, the Hessian, and the Fisher information matrix for linear dynamic systems
- A maximum likelihood algorithm for the mean and covariance of nonidentically distributed observations
- Estimation and tests of hypotheses for the initial mean and covariance in the kalman filter model
- A cutting plane algorithm for solving bilinear programs
- Linear system identification from nonstationary cross-sectional data
- Introduction to Stochastic Search and Optimization
- On Gradient-Based Search for Multivariable System Estimates
- A Statistical Approach to Thermal Management of Data Centers Under Steady State and System Perturbations
- Linear Statistical Inference and its Applications
- Concavity cuts for disjoint bilinear programming
- Convergence of a block coordinate descent method for nondifferentiable minimization
This page was built for publication: Cyclic seesaw process for optimization and identification