We study contextual stochastic optimization problems, where we leverage rich auxiliary observations (e.g., product characteristics) to improve decision making with uncertain variables demand). show how train forest policies for this problem by growing trees that choose splits directly optimize the downstream quality rather than split prediction accuracy as in standard random algorithm. realize ...

We discuss a general approach to building non-asymptotic confidence bounds for stochastic optimization problems. Our principal contribution is the observation that a Sample Average Approximation of a problem supplies upper and lower bounds for the optimal value of the problem which are essentially better than the quality of the corresponding optimal solutions. At the same time, such bounds are ...

We propose a new stochastic L-BFGS algorithm and prove a linear convergence rate for strongly convex and smooth functions. Our algorithm draws heavily from a recent stochastic variant of L-BFGS proposed in Byrd et al. (2014) as well as a recent approach to variance reduction for stochastic gradient descent from Johnson and Zhang (2013). We demonstrate experimentally that our algorithm performs ...

We propose a deterministic approach for global optimization of large-scale nonconvex quasiseparable problems encountered frequently in engineering systems design, such as multidisciplinary design optimization and product family optimization applications. Our branch and bound-based approach applies Lagrangian decomposition to 1) generate tight lower bounds by exploiting the structure of the prob...

We present a reformulation of stochastic global optimization as a filtering problem. The motivation behind this reformulation comes from the fact that for many optimization problems we cannot evaluate exactly the objective function to be optimized. Similarly, we may not be able to evaluate exactly the functions involved in iterative optimization algorithms. For example, we may only have access ...

The sample average approximation approach to solving stochastic programs induces a sampling error, caused by replacing an expectation by a sample average, as well as an optimization error due to approximating the solution of the resulting sample average problem. We obtain estimators of an optimal solution and the optimal value of the original stochastic program after executing a finite number o...

This study introduces the application of Harmony Search (HS) method for solving the parameter estimation problem in vapor-liquid equilibrium data modeling. The performance of this novel stochastic optimization strategy has been tested using several sets of binary vaporliquid equilibrium data with local composition models and the classical approach of least squares. Our results indicate that HS ...

this paper studies a maintenance policy for a system composed of two components, which are subject to continuous deterioration and consequently stochastic failure. the failure of each component results in the failure of the system. the components are inspected periodically and their deterioration degrees are monitored. the components can be maintained using different maintenance actions (repair...

The paper presents a general-purpose algorithm for solving stochastic combinatorial optimization problems with the expected value of a random variable as objective and deterministic constraints. The algorithm follows the Ant Colony Optimization (ACO) approach and uses Monte-Carlo sampling for estimating the objective. It is shown that on rather mild conditions, including that of linear incremen...