STOCHASTIC PROGRAMMING

Name: STOCHASTIC PROGRAMMING
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Rating: 4.3 (207 reviews)

COMPSCI/ISYE 719

과목 설명

Stochastic programming is concerned with decision making in the presence of uncertainty, where the eventual outcome depends on a future random event. Topics include modeling uncertainty in optimization problems, risk measures, stochastic programming algorithms, approximation and sampling methods, and applications. Students are strongly encouraged to have knowledge of linear programming (e.g.,MATH/COMP SCI/I SY E/STAT 525) and probability and statistics (e.g.,MATH/STAT 431). Knowledge of integer optimization (MATH/COMP SCI/I SY E 728) is helpful, but not required.

선수과목

Graduate/professional standing

충족 요건

This course does not satisfy any prerequisites.

학점

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미보고

평점

3.68

5.7% 과거 데이터 대비

수료율

100%

과거 데이터 대비 변화 없음

A 비율

70.59%

2.18% 과거 데이터 대비

학급 규모

-17.87% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

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유사 과목

STOCHASTIC COMPUTATIONAL METHODS

MATH 717

Introduction to computational methods that use stochastic algorithms and/or methods that are applied to random or stochastic mathematical problems. The main emphasis will be placed on learning practical tools, while some aspects of theoretical foundations will also be covered (e.g., basic error analysis for numerical solution of stochastic differential equations (SDEs), and basic convergence of Monte Carlo methods). Topics include Monte Carlo methods, Bayesian inference and Bayesian sampling, simulation of Markov chains, numerical analysis for SDEs, data assimilation / state estimation, stochastic optimization methods and random sketching. Applications to science, engineering, finance, data science, and other practical problems also included.

DYNAMIC PROGRAMMING AND ASSOCIATED TOPICS

COMPSCI/ISYE 723

General and special techniques of dynamic programming developed by means of examples. Shortest-path algorithms. Deterministic equipment replacement models. Resource allocation problem. Traveling-salesman problem. Knapsack problem. Analysis of inventory systems. General stochastic formulations. Markovian decision processes. Students are strongly encouraged to have knowledge of mathematical optimization (e.g.,COMP SCI/I SY E/MATH/STAT 525,I SY E 623,COMP SCI/I SY E/MATH/STAT 726), knowledge of analysis (e.g.,MATH/STAT 431or521) and programming ability (e.g.,COMP SCI 200or 301)

SPECIAL TOPICS IN OPERATIONS RESEARCH

ISYE 823

Subjects vary.

STOCHASTIC MODELING TECHNIQUES

ISYE 624

Techniques for modeling systems in which uncertainty is an essential factor. Emphasizes why, how and when techniques can or cannot be applied, rather than their mathematical derivation. Case studies and/or examples from such areas as logistics, production, and service industries.

ADVANCED ALGORITHMS

COMPSCI 787

Advanced paradigms for the design and analysis of efficient algorithms, including the use of randomness, linear programming, and semi-definite programming. Applications to data structures, approximating NP-hard optimization problems, learning, on-line and distributed problems. Students are strongly encouraged to have introductory knowledge of algorithms (e.g.,COMP SCI 577)