Pages that link to "Item:Q4327907"

The following pages link to On a class of algorithms from experimental design theory (Q4327907):

Displaying 20 items.

• Optimal design of multi-response experiments using semi-definite programming (Q374619) (← links)
• How experimental algorithmics can benefit from Mayo's extensions to Neyman-Pearson theory of testing (Q935029) (← links)
• D-optimal design methods for robust estimation of multivariate location and scatter (Q1299089) (← links)
• Designing algorithms by expectations (Q1328761) (← links)
• Algorithms for approximate linear regression design with application to a first order model with heteroscedasticity (Q1621395) (← links)
• Quasi-Newton algorithm for optimal approximate linear regression design: optimization in matrix space (Q1644428) (← links)
• An algorithmic framework for generating optimal two-stratum experimental designs (Q1658390) (← links)
• Optimal and robust invariant designs for cubic multiple regression (Q1805527) (← links)
• Computing \(E\)-optimal polynomial regression designs. (Q1817298) (← links)
• Component-wise dimension reduction (Q1874088) (← links)
• Algorithms for optimal design with application to multiple polynomial regression (Q1902155) (← links)
• Computing optimal approximate invariant designs for cubic regression on multidimensional balls and cubes (Q1907641) (← links)
• An overview of design of experiments (Q1915111) (← links)
• Polars and subgradients of mixtures of information functions (Q2365871) (← links)
• Construction of optimal designs using a clustering approach (Q2581666) (← links)
• Limits to measurement in experiments governed by algorithms (Q3061157) (← links)
• Tuning an Algorithm Using Design of Experiments (Q3064524) (← links)
• A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments (Q3304857) (← links)
• Optimisation in space of measures and optimal design (Q4452118) (← links)
• Second order methods for solving extremum problems form optimal linar regression design (Q4893714) (← links)