This paper presents an effective way of combining two popular, yet distinct approaches used in the hedging literature – dynamic programming (DP) and time-series (GARCH) econometrics. Theoretically consistent yet realistic and tractable models are developed for traders interested in hedging a portfolio. Results from a bootstrapping experiment used to construct confidence bands around the competi...

We present the first provably optimal polynomial time dynamic programming (DP) algorithm for best-first shift-reduce parsing, which applies the DP idea of Huang and Sagae (2010) to the best-first parser of Sagae and Lavie (2006) in a non-trivial way, reducing the complexity of the latter from exponential to polynomial. We prove the correctness of our algorithm rigorously. Experiments confirm th...

Given a set of n points (nodes) on a line and a set of m weighted intervals defined on the nodes, we consider a particular dynamic programming (DP) problem on these intervals. If the weight function of the DP has convex or concave property, we can solve this DP problem efficiently by using matrix searching in Monge matrices, together with a new query data structure, which we call the consecutiv...

A number of automatic lexicon construction methods have been proposed in recent years. Such approaches employ a dynamic programming (DP) match to collect statistics concerning differences between the observed phone sequence and that which was predicted by a standard lexicon. A more expressive lexicon is then constructed based upon the collected statistics for use within a speech recognition sys...

Dynamic Programming (DP) is an important class of algorithms widely used in many areas of speech and language processing. Recently there have been a series of work trying to formalize many instances of DP algorithms under algebraic and graph-theoretic frameworks. This tutorial surveys two such frameworks, namely semirings and directed hypergraphs, and draws connections between them. We formaliz...

While sequential pattern mining (SPM) is an import application in uncertain databases, it is challenging in efficiency and scalability. In this paper, we develop a dynamic programming (DP) approach to mine probabilistic frequent sequential patterns in distributed computing platform Spark. Directly applying the DP method to Spark is impractical because its memory-consuming characteristic may cau...

Most contemporary database systems query optimizers exploit System-R’s dynamic programming method (DP) to find the optimal query execution plan (QEP) without evaluating redundant sub-plans. However, in the relational database setting today, large queries containing many joins are becoming increasingly common. Based on this trend, it has become temping to improve the DP performance. Chip Multi-P...

A number of automatic lexicon construction methods have been proposed in recent years. Such approaches employ a dynamic programming (DP) match to collect statistics concerning differences between the observed phone sequence and that which was predicted by a standard lexicon. A more expressive lexicon is then constructed based upon the collected statistics, offering a more accurate phone-to-word...

The great challenge of the agricultural sector is to produce more food and/or more revenue from less water, which can be achieved by optimal irrigation management. A task of primary importance is the problem of intraseasonal irrigation scheduling under limited seasonal water supply. On the intraseasonal level, a limited amount of water is to be distributed over a number of irrigations, taking i...

Developments in robust model predictive control are reviewed from a perspective gained through a personal involvement in the research area during the past two decades. Various min–max MPC formulations are discussed in the setting of optimizing the “worst-case” performance in closed loop. One of the insights gained is that the conventional open-loop formulation of MPC is fundamentally flawed to ...