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

We introduce an extension of Dual Dynamic Programming (DDP) to solve linear dynamic programming equations. We call this extension IDDP-LP which applies to situations where some or all primal and dual subproblems to be solved along the iterations of the method are solved with a bounded error (inexactly). We provide convergence theorems both in the case when errors are bounded and for asymptotically vanishing errors. We extend the analysis to stochastic linear dynamic programming equations, introducing Inexact Stochastic Dual Dynamic Programming for linear programs (ISDDP-LP), an inexact variant of SDDP applied to linear programs corresponding to the situation where some or all problems to be solved in the forward and backward passes of SDDP are solved approximately. We also provide convergence theorems for ISDDP-LP for bounded and asymptotically vanishing errors. Finally, we present the results of numerical experiments comparing SDDP and ISSDP-LP on a portfolio problem with direct transation costs modelled as a multistage stochastic linear optimization problem. On these experiments, for some values of the noises, ISDDP-LP can converge significantly quicker than SDDP.

Inexact Cuts in Deterministic and Stochastic Dual Dynamic Programming Applied to Linear Optimization Problems