ADVANCED LINEAR PROGRAMMING

Name: ADVANCED LINEAR PROGRAMMING
Availability: OutOfStock

COMPSCI/ISYE 526

과목 설명

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.

선수과목

COMPSCI/ISYE/MATH/STAT 525 and (COMPSCI 200 , COMPSCI 220 , COMPSCI 300 , 301, 302, COMPSCI 310 , or placement into COMPSCI 300 ) or graduate/professional standing

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점

2.81

2.27% 과거 데이터 대비

수료율

100%

14.29% 과거 데이터 대비

A 비율

37.5%

50% 과거 데이터 대비

학급 규모

50% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

INTEGER OPTIMIZATION

COMPSCI/ISYE/MATH 728

Introduces optimization problems over integers, and surveys the theory behind the algorithms used in state-of-the-art methods for solving such problems. Special attention is given to the polyhedral formulations of these problems, and to their algebraic and geometric properties. Applicability of Integer Optimization is highlighted with applications in combinatorial optimization. Key topics include: formulations, relaxations, polyhedral theory, cutting planes, decomposition, enumeration. Students are strongly encouraged to have knowledge of Linear Programming (e.g.,MATH/COMP SCI/I SY E/STAT 525), including algorithms, duality and polyhedral theory.

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)

INTRODUCTION TO OPTIMIZATION

COMPSCI/ECE/ISYE 524

Introduction to mathematical optimization from a modeling and solution perspective. Formulation of applications as discrete and continuous optimization problems and equilibrium models. Survey and appropriate usage of basic algorithms, data and software tools, including modeling languages and subroutine libraries.

LINEAR OPTIMIZATION

COMPSCI/ISYE/MATH/STAT 525

Introduces optimization problems whose constraints are expressed by linear inequalities. Develops geometric and algebraic insights into the structure of the problem, with an emphasis on formal proofs. Presents the theory behind the simplex method, the main algorithm used to solve linear optimization problems. Explores duality theory and theorems of the alternatives.

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)