English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A matrix optimization problem over an uncertain linear system on finitehorizon (abbreviated as MOPUL) is studied, in which the uncertain transitionmatrix is regarded as a decision variable. This problem is in general NP-hard.By using the given reference values of system outputs at each stage, we developa polynomial-time solvable semidefinite programming (SDP) approximation modelfor the problem. The upper bound of the cumulative error between referenceoutputs and the optimal outputs of the approximation model is theoreticallyanalyzed. Two special cases associated with specific applications areconsidered. The quality of the SDP approximate solutions in terms offeasibility and optimality is also analyzed. Results of numerical experimentsare presented to show the influences of perturbed noises at reference outputsand control levels on the performance of SDP approximation.

Semidefinite Programming Approximation for a Matrix Optimization Problem over an Uncertain Linear System