Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis.

Output analysis for approximated stochastic programs.- Combinatorial Randomized Rounding: Boosting Randomized Rounding with Combinatorial Arguments.- Statutory Regulation of Casualty Insurance Companies: An Example from Norway with Stochastic Programming Analysis.- Option pricing in a world with arbitrage.- Monte Carlo Methods for Discrete Stochastic Optimization.- Discrete Approximation in Quantile Problem of Portfolio Selection.- Optimizing electricity distribution using two-stage integer recourse models.- A Finite-Dimensional Approach to Infinite-Dimensional Constraints in Stochastic Programming Duality.- Non—Linear Risk of Linear Instruments.- Multialgorithms for Parallel Computing: A New Paradigm for Optimization.- Convergence Rate of Incremental Subgradient Algorithms.- Transient Stochastic Models for Search Patterns.- Value-at-Risk Based Portfolio Optimization.- Combinatorial Optimization, Cross-Entropy, Ants and Rare Events.- Consistency of Statistical Estimators: the Epigraphical View.- Hierarchical Sparsity in Multistage Convex Stochastic Programs.- Conditional Value-at-Risk: Optimization Approach.

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