Parametric concavity in stochastic dynamic programs

Ganesh Janakiraman, Sridhar Seshadri

Research output: Contribution to journalArticlepeer-review


e study a family of dynamic programs that are characterized by a deterministic vector of cost parameters. We show that if the single period cost function is concave with respect to this vector, then the optimal costs of the family of dynamic programs are also concave in the vector of costs. We also establish that the optimal cost inherits other properties, namely, super-additivity, +∞-star-shaped, 0-star-shaped, concavity-along-rays and monotonicity. When the vector of cost parameters evolves as a stochastic process and the single period cost is concave with respect to this vector, we show that the optimal cost is bounded above by the optimal cost for the dynamic program in which these stochastic cost parameters are replaced by their expectations in each period. We provide examples to illustrate how our results can be used to derive bounds which are either easy to compute or have analytical expressions. We also explain why such bounds are useful.

Original languageEnglish (US)
Pages (from-to)98-102
Number of pages5
JournalComputers and Industrial Engineering
Issue number1
StatePublished - Aug 2011
Externally publishedYes


  • Concavity
  • Dynamic programming
  • Inventory

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)


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