H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Mathematics H-index 73 Citations 31,491 189 World Ranking 97 National Ranking 56
Engineering and Technology H-index 65 Citations 27,879 140 World Ranking 411 National Ranking 180

Research.com Recognitions

Awards & Achievements

2020 - Member of the National Academy of Engineering For contributions to the theory, computation, and application of stochastic programming.

2018 - Dantzig Prize, by the Society for Industrial and Applied Mathematics (SIAM) and the Mathematical Optimization Society (MOS)

2013 - Khachiyan Prize of the INFORMS Optimization Society

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Normal distribution
  • Mathematical optimization

His primary scientific interests are in Mathematical optimization, Stochastic programming, Monte Carlo method, Optimization problem and Stochastic optimization. His study in Mathematical optimization focuses on Dynamic programming in particular. His Stochastic programming study combines topics from a wide range of disciplines, such as Saddle point, Discrete optimization, Stochastic modelling and Applied mathematics.

His Monte Carlo method research incorporates elements of Structure, Statistical hypothesis testing, Statistical inference and Stochastic approximation. The concepts of his Optimization problem study are interwoven with issues in Expected value and Optimal control. His Stochastic optimization research includes themes of Constraint programming, Convex analysis and Average-case complexity.

His most cited work include:

  • Perturbation Analysis of Optimization Problems (1704 citations)
  • Robust Stochastic Approximation Approach to Stochastic Programming (1492 citations)
  • Lectures on Stochastic Programming: Modeling and Theory (1317 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Mathematical optimization, Stochastic programming, Applied mathematics, Optimization problem and Stochastic optimization. In his study, Risk aversion is strongly linked to Time consistency, which falls under the umbrella field of Mathematical optimization. As a part of the same scientific study, Alexander Shapiro usually deals with the Stochastic programming, concentrating on Estimator and frequently concerns with Statistical inference and Econometrics.

His Applied mathematics study also includes

  • Mathematical analysis which intersects with area such as Nonlinear programming,
  • Asymptotic distribution that connect with fields like Asymptotic analysis. He works mostly in the field of Optimization problem, limiting it down to topics relating to Duality and, in certain cases, Semidefinite programming, as a part of the same area of interest. He regularly links together related areas like Stochastic approximation in his Stochastic optimization studies.

He most often published in these fields:

  • Mathematical optimization (44.02%)
  • Stochastic programming (25.48%)
  • Applied mathematics (22.01%)

What were the highlights of his more recent work (between 2012-2021)?

  • Mathematical optimization (44.02%)
  • Stochastic programming (25.48%)
  • Time consistency (5.79%)

In recent papers he was focusing on the following fields of study:

Alexander Shapiro mostly deals with Mathematical optimization, Stochastic programming, Time consistency, Applied mathematics and Dynamic programming. He interconnects Mathematical economics and Dual in the investigation of issues within Mathematical optimization. His Stochastic programming research is multidisciplinary, incorporating elements of Robust optimization, Risk neutral, Risk aversion and Probability measure.

Alexander Shapiro has included themes like Dynamic inconsistency, Relation, Econometrics and Risk measure in his Time consistency study. His work deals with themes such as Function, Covariance, Sample and Goodness of fit, which intersect with Applied mathematics. His research integrates issues of Stochastic modelling, Bellman equation, Markov decision process, Markov chain and Volatility in his study of Dynamic programming.

Between 2012 and 2021, his most popular works were:

  • Risk neutral and risk averse Stochastic Dual Dynamic Programming method (144 citations)
  • Lectures on Stochastic Programming: Modeling and Theory, Second Edition (141 citations)
  • On Kusuoka Representation of Law Invariant Risk Measures (71 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Normal distribution
  • Mathematical analysis

His scientific interests lie mostly in Stochastic programming, Mathematical optimization, Dynamic programming, Time consistency and Mathematical economics. The concepts of his Stochastic programming study are interwoven with issues in Sample, Stochastic optimization, Probability measure and Minification. Alexander Shapiro combines subjects such as Structure, Stochastic approximation, Saddle point, Monte Carlo method and Subgradient method with his study of Stochastic optimization.

His Optimization problem study in the realm of Mathematical optimization connects with subjects such as Invariant. His Optimization problem research is multidisciplinary, relying on both Metric and Convex set. The Dynamic programming study combines topics in areas such as Stochastic modelling, Markov decision process, Risk neutral and Dual.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Lectures on Stochastic Programming: Modeling and Theory

Alexander Shapiro;Darinka Dentcheva;Andrzej P. Ruszczyński.
(2009)

3180 Citations

Perturbation Analysis of Optimization Problems

J Frédéric Bonnans;Alexander Shapiro.
(2000)

2597 Citations

Stochastic Approximation approach to Stochastic Programming

Anatoli Juditsky;Guanghui Lan;Arkadii S. Nemirovski;Alexander Shapiro.
(2013)

1872 Citations

Robust Stochastic Approximation Approach to Stochastic Programming

A. Nemirovski;A. Juditsky;G. Lan;A. Shapiro.
Siam Journal on Optimization (2008)

1868 Citations

The Sample Average Approximation Method for Stochastic Discrete Optimization

Anton J. Kleywegt;Alexander Shapiro;Tito Homem-de-Mello.
Siam Journal on Optimization (2002)

1699 Citations

A stochastic programming approach for supply chain network design under uncertainty

Tjendera Santoso;Shabbir Ahmed;Marc Goetschalckx;Alexander Shapiro.
European Journal of Operational Research (2005)

1318 Citations

Value-at-Risk-Based Risk Management: Optimal Policies and Asset Prices

Suleyman Basak;Alexander Shapiro.
Review of Financial Studies (2001)

1165 Citations

Convex Approximations of Chance Constrained Programs

Arkadi Nemirovski;Alexander Shapiro.
Siam Journal on Optimization (2006)

1028 Citations

Monte Carlo Sampling Methods

Alexander Shapiro.
Handbooks in Operations Research and Management Science (2003)

719 Citations

On the multivariate asymptotic distribution of sequential Chi-square statistics.

James H. Steiger;Alexander Shapiro;Michael W. Browne.
Psychometrika (1985)

647 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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