DYNAMIC PROGRAMMING AND ASSOCIATED TOPICS

Name: DYNAMIC PROGRAMMING AND ASSOCIATED TOPICS
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Rating: 4.5 (141 reviews)

COMPSCI/ISYE 723

과목 설명

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)

선수과목

Graduate/professional standing

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점

3.95

10.52% 과거 데이터 대비

수료율

100%

과거 데이터 대비 변화 없음

A 비율

90.91%

28.18% 과거 데이터 대비

학급 규모

71.63% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

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유사 과목

INTRODUCTION TO COMBINATORIAL OPTIMIZATION

COMPSCI/ISYE/MATH 425

Focuses on optimization problems over discrete structures, such as shortest paths, spanning trees, flows, matchings, and the traveling salesman problem. We will investigate structural properties of these problems, and we will study both exact methods for their solution, and approximation algorithms.

ADVANCED LINEAR PROGRAMMING

COMPSCI/ISYE 526

Review of linear programming. Polynomial time methods for linear programming. Quadratic programs and linear complementarity problems and related solution techniques. Solution sets and their continuity properties. Error bounds for linear inequalities and programs. Parallel algorithms for linear and quadratic programs.

SPECIAL TOPICS IN OPERATIONS RESEARCH

ISYE 823

Subjects vary.

STOCHASTIC PROGRAMMING

COMPSCI/ISYE 719

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.

OPERATIONS RESEARCH-DETERMINISTIC MODELING

ISYE 323

Basic techniques for modeling and optimizing deterministic systems with emphasis on linear programming. Computer solution of optimization problems. Applications to production, logistics, and service systems.