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Browse materialsMIT 1.204 · Spring 2010

Computer Algorithms in Systems Engineering

Subject: Engineering

Art: Zhong you he gong quan tu. LOC gm71002470, 1898 · Public domain

Instructor: Dr. George Kocur
Term: Spring 2010
Level: Graduate
What's inside: 1 lecture note · 1 reading · 66 other files · 5 web pages
Materials open: MIT OCW

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About this course

Source: MIT OpenCourseWare 1.204 Computer Algorithms in Systems Engineering, Spring 2010, Dr. George Kocur — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

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How this course was organized

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A lecture sequence of 24 items is confirmed. Assignment and exam materials exist too, but MIT's own order for them could not be verified, so they're shown as materials below rather than a sequence.

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Materials by section

Grouped by section, as MIT organized them. Not sequenced or ordered.

Lecture Notes

49 files

Assignments

14 files

Exams

MIT OpenCourseWare, Dr. George Kocur, 1.204 Computer Algorithms in Systems Engineering, Spring 2010.

License: CC BY-NC-SA 4.0 · Edition: Spring 2010 · Open the original course on OCW ↗

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Computer Algorithms in Systems Engineering

Before you start

What's here, and what isn't

Not included here

Materials by section

Lecture Notes

Assignments

Exams

Lecture 1: Database: Entity-relationship modeling

Lecture 2: Database: Normalization, SQL basics

Lecture 3: Database: SQL joins, views, subqueries

Lecture 4: Database: JDBC

Lecture 5: Algorithms: Analysis, complexity

Lecture 6: Stacks, queues, trees, dictionaries

Lecture 7: Graphs

Lecture 8: Heaps, sets

Lecture 9: Divide-conquer: Binary search, quicksort, selection

Lecture 10: Greedy: Knapsack, job sequence

Lecture 11: Greedy: Minimum spanning trees

Lecture 12: Greedy/dynamic programming: Shortest paths

Lecture 13: Priniciple of optimality, resource allocation

Lecture 14: Job scheduling, graph/tree generation

Lecture 15: Knapsack problem, set representation

Lecture 16: Branch and bound: Backtrack method, knapsack problem

Lecture 17: Branch and bound: General method, facility location

Lecture 18: Continuous constrained nonlinear optimization

Lecture 19: Network equilibrium

Lecture 20: Linear systems

Lecture 21: Continuous unconstrained nonlinear optimization

Lecture 22: Unconstrained methods: Amoeba, BFGS, demand model estimation

Lecture 23: Analytic approximations: Network design

Lecture 24: Approximate queuing analysis