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Description

This course offers an introduction to optimization problems, algorithms, and their complexity, emphasizing basic methodologies and the underlying mathematical structures. The main topics covered include: Theory and algorithms for linear programming Network flow problems and algorithms Introduction to integer programming and combinatorial problems This course offers an introduction to optimization problems, algorithms, and their complexity, emphasizing basic methodologies and the underlying mathematical structures. The main topics covered include: Theory and algorithms for linear programming Network flow problems and algorithms Introduction to integer programming and combinatorial problemsSubjects

optimization | optimization | algorithms | algorithms | linear programming | linear programming | network flow problems | network flow problems | integer programming | integer programming | combinatorial problems | combinatorial problems | mathematics | mathematics | mathematical programming | mathematical programming | 6.251 | 6.251 | 15.081 | 15.081License

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Imagine you are a salesman needing to visit 100 cities connected by a set of roads. Can you do it while stopping in each city only once? Even a supercomputer working at 1 trillion operations per second would take longer than the age of the universe to find a solution when considering each possibility in turn. In 1994, Leonard Adleman published a paper in which he described a solution, using the tools of molecular biology, for a smaller 7-city example of this problem. His paper generated enormous scientific and public interest, and kick-started the field of Biological Computing, the main subject of this discussion based seminar course. Students will analyze the Adleman paper, and the papers that preceded and followed it, with an eye for identifying the engineering and scientific aspects of Imagine you are a salesman needing to visit 100 cities connected by a set of roads. Can you do it while stopping in each city only once? Even a supercomputer working at 1 trillion operations per second would take longer than the age of the universe to find a solution when considering each possibility in turn. In 1994, Leonard Adleman published a paper in which he described a solution, using the tools of molecular biology, for a smaller 7-city example of this problem. His paper generated enormous scientific and public interest, and kick-started the field of Biological Computing, the main subject of this discussion based seminar course. Students will analyze the Adleman paper, and the papers that preceded and followed it, with an eye for identifying the engineering and scientific aspects ofSubjects

biological computing | biological computing | Leonard Adleman | Leonard Adleman | exquisite detection | exquisite detection | whole-cell computing | whole-cell computing | computation | computation | molecular biology | molecular biology | biotin-avidin | biotin-avidin | magnetic beads | magnetic beads | cellular processes | cellular processes | combinatorial problems | combinatorial problems | self-assembly | self-assembly | nanodevices | nanodevices | molecular machines | molecular machines | quorum sensing | quorum sensing | molecular switches | molecular switches | ciliates | ciliates | molecular gates | molecular gates | molecular circuits | molecular circuits | genetic switch | genetic switch | cellular networks | cellular networks | genetic networks | genetic networks | genetic circuits | genetic circuitsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata6.251J Introduction to Mathematical Programming (MIT)

Description

This course offers an introduction to optimization problems, algorithms, and their complexity, emphasizing basic methodologies and the underlying mathematical structures. The main topics covered include: Theory and algorithms for linear programming Network flow problems and algorithms Introduction to integer programming and combinatorial problemsSubjects

optimization | algorithms | linear programming | network flow problems | integer programming | combinatorial problems | mathematics | mathematical programming | 6.251 | 15.081License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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See all metadata7.349 Biological Computing: At the Crossroads of Engineering and Science (MIT)

Description

Imagine you are a salesman needing to visit 100 cities connected by a set of roads. Can you do it while stopping in each city only once? Even a supercomputer working at 1 trillion operations per second would take longer than the age of the universe to find a solution when considering each possibility in turn. In 1994, Leonard Adleman published a paper in which he described a solution, using the tools of molecular biology, for a smaller 7-city example of this problem. His paper generated enormous scientific and public interest, and kick-started the field of Biological Computing, the main subject of this discussion based seminar course. Students will analyze the Adleman paper, and the papers that preceded and followed it, with an eye for identifying the engineering and scientific aspects ofSubjects

biological computing | Leonard Adleman | exquisite detection | whole-cell computing | computation | molecular biology | biotin-avidin | magnetic beads | cellular processes | combinatorial problems | self-assembly | nanodevices | molecular machines | quorum sensing | molecular switches | ciliates | molecular gates | molecular circuits | genetic switch | cellular networks | genetic networks | genetic circuitsLicense

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htmSite sourced from

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