Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

This book provides a complete and comprehensive reference/guide to Pyomo (Python Optimization Modeling Objects) for both beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. The text illustrates the breadth of the modeling and analysis capabilities that are supported by the software and support of complex real-world applications. Pyomo is an open source software package for formulating and solving large-scale optimization and operations research problems. The text begins with a tutorial on simple linear and integer programming models. A detailed reference of Pyomo's modeling components is illustrated with extensive examples, including a discussion of how to load data from data sources like spreadsheets and databases. Chapters describing advanced modeling capabilities for nonlinear and stochastic optimization are also included. The Pyomo software provides familiar modeling features within Python, a powerful dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions. The software supports a different modeling approach than commercial AML (Algebraic Modeling Languages) tools, and is designed for flexibility, extensibility, portability, and maintainability but also maintains the central ideas in modern AMLs.

https://calhoun.nps.edu/bitstream/10945/37919/1/Dimitrov-PyomoReview.pdf

Pyomo - Optimization Modeling in Python

Risk-averse two-stage stochastic programming with an application to disaster management

In classical two-stage stochastic programming the expected value of the total costs is minimized. Recently, mean-risk models - studied in mathematical finance for several decades - have attracted attention in stochastic programming. We consider Conditional Value-at-Risk as risk measure in the framework of two-stage stochastic integer programming. The paper addresses structure, stability, and algorithms for this class of models. In particular, we study continuity properties of the objective function, both with respect to the first-stage decisions and the integrating probability measure. Further, we present an explicit mixed-integer linear programming formulation of the problem when the probability distribution is discrete and finite. Finally, a solution algorithm based on Lagrangean relaxation of nonanticipativity is proposed.

/pdf/conditional-value-at-risk-in-stochastic-programs-with-mixed-37kwkzniq6.pdf

Conditional Value-at-Risk in Stochastic Programs with Mixed-Integer Recourse

Including integer variables into traditional stochastic linear programs has considerable implications for structural analysis and algorithm design. Starting from mean-risk approaches with different risk measures we identify corresponding two- and multi-stage stochastic integer programs that are large-scale block-structured mixed-integer linear programs if the underlying probability distributions are discrete. We highlight the role of mixed-integer value functions for structure and stability of stochastic integer programs. When applied to the block structures in stochastic integer programming, well known algorithmic principles such as branch-and-bound, Lagrangian relaxation, or cutting plane methods open up new directions of research. We review existing results in the field and indicate departure points for their extension.

Stochastic programming with integer variables

We consider linear two-stage stochastic programs with mixed-integer recourse. Instead of basing the selection of an optimal first-stage solution on expected costs alone, we include into the objective a risk term reflecting the probability that a preselected cost threshold is exceeded. After we have put the resulting mean-risk model into perspective with stochastic dominance, we study further structural properties of the model and derive some basic stability results. In the algorithmic part of the paper, we propose a scenario decomposition method and report initial computational experience.

/pdf/risk-aversion-via-excess-probabilities-in-stochastic-1sxnzrsqz8.pdf

Stephan Tiedemann

Papers

Conditional Value-at-Risk in Stochastic Programs with Mixed-Integer Recourse

Risk Aversion via Excess Probabilities in Stochastic Programs with Mixed-Integer Recourse