**Are you an EPFL student looking for a semester project?**

Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.

Publication# Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Abstract

Stochastic optimization is a popular modeling paradigm for decision-making under uncertainty and has a wide spectrum of applications in management science, economics and engineering. However, the stochastic optimization models one faces in practice are intractable, and numerical solutions necessitate approximations. The mainstream approach for making a stochastic optimization model amenable to numerical solution is to discretize the probability distribution of the uncertain problem parameters. However, both the accuracy of the approximation as well as the computational burden of solving the approximate problem scale with the number of scenarios of the approximate distribution. An effective means to ease the computational burden is to use scenario reduction, which replaces an accurate initial distribution accommodating many scenarios with a simpler distribution supported on only few scenarios that is close to the initial distribution with respect to a probability metric. Using the Wasserstein distance as measure of proximity between distributions, we provide new insights into the fundamental limitations of scenario reduction, and we propose the first polynomial-time constant-factor approximations for a popular scenario reduction problem from the literature. As scenario reduction is equivalent to clustering, it suffers from two well-known shortcomings. Namely, it suffers from outlier sensitivity and may produce highly unbalanced clusters. To mitigate both shortcomings, we formulate a joint outlier detection and clustering problem, where the clusters must satisfy certain cardinality constraints. We cast this problem as a mixed-integer linear program (MILP) that admits tractable semidefinite and linear programming relaxations. We propose deterministic rounding schemes that transform the relaxed solutions to feasible solutions for the MILP. We also prove that these solutions are optimal in the MILP if a cluster separation condition holds. Finally, we develop a highly efficient scenario reduction method for a large-scale hydro scheduling problem. Specifically, we study the optimal operation of a fleet of interconnected hydropower plants that sell energy on both the spot and the reserve markets, and we propose a two-layer stochastic programming framework for its solution. The outer layer problem (the planner's problem) optimizes the end-of-day reservoir filling levels over one year, whereas the inner layer problem (the trader's problem) selects optimal hourly market bids within each day. We prove that the trader's problem admits a scenario reduction that dramatically reduces its complexity without loss of optimality, which in turn facilitates an efficient approximation of the planner's problem.

Official source

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related MOOCs (28)

Advanced statistical physics

We explore statistical physics in both classical and open quantum systems. Additionally, we will cover probabilistic data analysis that is extremely useful in many applications.

Advanced statistical physics

We explore statistical physics in both classical and open quantum systems. Additionally, we will cover probabilistic data analysis that is extremely useful in many applications.

Selected Topics on Discrete Choice

Discrete choice models are used extensively in many disciplines where it is important to predict human behavior at a disaggregate level. This course is a follow up of the online course “Introduction t

Related concepts (40)

Related publications (165)

Linear programming relaxation

In mathematics, the relaxation of a (mixed) integer linear program is the problem that arises by removing the integrality constraint of each variable. For example, in a 0–1 integer program, all constraints are of the form The relaxation of the original integer program instead uses a collection of linear constraints The resulting relaxation is a linear program, hence the name.

Linear programming

Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships. Linear programming is a special case of mathematical programming (also known as mathematical optimization). More formally, linear programming is a technique for the optimization of a linear objective function, subject to linear equality and linear inequality constraints.

Integer programming

An integer programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers. In many settings the term refers to integer linear programming (ILP), in which the objective function and the constraints (other than the integer constraints) are linear. Integer programming is NP-complete. In particular, the special case of 0-1 integer linear programming, in which unknowns are binary, and only the restrictions must be satisfied, is one of Karp's 21 NP-complete problems.

Pierre Vandergheynst, Milos Vasic, Francesco Craighero, Renata Khasanova

Under resource constraints, LLMs are usually fine- tuned with additional knowledge using Parameter Efficient Fine-Tuning (PEFT), using Low-Rank Adaptation (LoRA) modules. In fact, LoRA injects a new set of small trainable matrices to adapt an LLM to a new ...

2024Pierre Vandergheynst, Milos Vasic, Francesco Craighero, Renata Khasanova

Under resource constraints, LLMs are usually fine-tuned with additional knowledge using Parameter Efficient Fine-Tuning (PEFT), using Low-Rank Adaptation (LoRA) modules. In fact, LoRA injects a new set of small trainable matrices to adapt an LLM to a new t ...

2024Volkan Cevher, Grigorios Chrysos, Efstratios Panteleimon Skoulakis

Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractional opti ...

2024