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6.856J Randomized Algorithms (MIT) 6.856J Randomized Algorithms (MIT)

Description

This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms. This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.Subjects

Randomized Algorithms | Randomized Algorithms | algorithms | algorithms | efficient in time and space | efficient in time and space | randomization | randomization | computational problems | computational problems | data structures | data structures | graph algorithms | graph algorithms | optimization | optimization | geometry | geometry | Markov chains | Markov chains | sampling | sampling | estimation | estimation | geometric algorithms | geometric algorithms | parallel and distributed algorithms | parallel and distributed algorithms | parallel and ditributed algorithm | parallel and ditributed algorithm | parallel and distributed algorithm | parallel and distributed algorithm | random sampling | random sampling | random selection of witnesses | random selection of witnesses | symmetry breaking | symmetry breaking | randomized computational models | randomized computational models | hash tables | hash tables | skip lists | skip lists | minimum spanning trees | minimum spanning trees | shortest paths | shortest paths | minimum cuts | minimum cuts | convex hulls | convex hulls | linear programming | linear programming | fixed dimension | fixed dimension | arbitrary dimension | arbitrary dimension | approximate counting | approximate counting | parallel algorithms | parallel algorithms | online algorithms | online algorithms | derandomization techniques | derandomization techniques | probabilistic analysis | probabilistic analysis | computational number theory | computational number theory | simplicity | simplicity | speed | speed | design | design | basic probability theory | basic probability theory | application | application | randomized complexity classes | randomized complexity classes | game-theoretic techniques | game-theoretic techniques | Chebyshev | Chebyshev | moment inequalities | moment inequalities | limited independence | limited independence | coupon collection | coupon collection | occupancy problems | occupancy problems | tail inequalities | tail inequalities | Chernoff bound | Chernoff bound | conditional expectation | conditional expectation | probabilistic method | probabilistic method | random walks | random walks | algebraic techniques | algebraic techniques | probability amplification | probability amplification | sorting | sorting | searching | searching | combinatorial optimization | combinatorial optimization | approximation | approximation | counting problems | counting problems | distributed algorithms | distributed algorithms | 6.856 | 6.856 | 18.416 | 18.416License

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See all metadata6.854J Advanced Algorithms (MIT) 6.854J Advanced Algorithms (MIT)

Description

This course is a first-year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms. Domains include string algorithms, network optimization, parallel algorithms, computational geometry, online algorithms, external memory, cache, and streaming algorithms, and data structures. This course is a first-year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms. Domains include string algorithms, network optimization, parallel algorithms, computational geometry, online algorithms, external memory, cache, and streaming algorithms, and data structures.Subjects

amortization | amortization | randomization | randomization | fingerprinting | fingerprinting | word-level parallelism | word-level parallelism | bit scaling | bit scaling | dynamic programming | dynamic programming | network flow | network flow | linear programming | linear programming | fixed-parameter algorithms | fixed-parameter algorithms | approximation algorithms | approximation algorithms | string algorithms | string algorithms | network optimization | network optimization | parallel algorithms | parallel algorithms | computational geometry | computational geometry | online algorithms | online algorithms | external memory | external memory | external cache | external cache | external streaming | external streaming | data structures | data structuresLicense

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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14.11 is a new class on the topic of field (that is, 'in situ') and laboratory experiments in the social sciences - both what these experiments have taught and can teach us and how to conduct them. 14.11 is a new class on the topic of field (that is, 'in situ') and laboratory experiments in the social sciences - both what these experiments have taught and can teach us and how to conduct them.Subjects

racial discrimination | racial discrimination | public health and persuasion | public health and persuasion | incentives | incentives | gender differences in economic environments | gender differences in economic environments | intrinsic motivation and fairness | intrinsic motivation and fairness | educational quality | educational quality | corruption | corruption | learning and social effects | learning and social effects | housing experiments | housing experiments | voting behavior and political economy | voting behavior and political economy | jury advocacy | jury advocacy | causal inference | causal inference | internal and external threats | internal and external threats | clustering | clustering | standard errors | standard errors | randomization | randomization | statistical inference with multiple outcomes | statistical inference with multiple outcomesLicense

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 metadata18.433 Combinatorial Optimization (MIT) 18.433 Combinatorial Optimization (MIT)

Description

Combinatorial Optimization provides a thorough treatment of linear programming and combinatorial optimization. Topics include network flow, matching theory, matroid optimization, and approximation algorithms for NP-hard problems. Combinatorial Optimization provides a thorough treatment of linear programming and combinatorial optimization. Topics include network flow, matching theory, matroid optimization, and approximation algorithms for NP-hard problems.Subjects

linear programming | linear programming | combinatorial optimization | combinatorial optimization | network flow | network flow | matching theory | matching theory | matroid optimization | matroid optimization | approximation algorithms for NP-hard problems | approximation algorithms for NP-hard problems | approximation algorithms | approximation algorithms | NP-hard problems | NP-hard problems | discrete mathematics | discrete mathematics | fundamental algorithmic techniques | fundamental algorithmic techniques | convex programming | convex programming | flow theory | flow theory | randomization | randomizationLicense

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 metadata18.433 Combinatorial Optimization (MIT) 18.433 Combinatorial Optimization (MIT)

Description

Combinatorial Optimization provides a thorough treatment of linear programming and combinatorial optimization. Topics include network flow, matching theory, matroid optimization, and approximation algorithms for NP-hard problems. Combinatorial Optimization provides a thorough treatment of linear programming and combinatorial optimization. Topics include network flow, matching theory, matroid optimization, and approximation algorithms for NP-hard problems.Subjects

linear programming | linear programming | combinatorial optimization | combinatorial optimization | network flow | network flow | matching theory | matching theory | matroid optimization | matroid optimization | approximation algorithms for NP-hard problems | approximation algorithms for NP-hard problems | approximation algorithms | approximation algorithms | NP-hard problems | NP-hard problems | discrete mathematics | discrete mathematics | fundamental algorithmic techniques | fundamental algorithmic techniques | convex programming | convex programming | flow theory | flow theory | randomization | randomizationLicense

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 metadata6.856J Randomized Algorithms (MIT)

Description

This course examines how randomization can be used to make algorithms simpler and more efficient via random sampling, random selection of witnesses, symmetry breaking, and Markov chains. Topics covered include: randomized computation; data structures (hash tables, skip lists); graph algorithms (minimum spanning trees, shortest paths, minimum cuts); geometric algorithms (convex hulls, linear programming in fixed or arbitrary dimension); approximate counting; parallel algorithms; online algorithms; derandomization techniques; and tools for probabilistic analysis of algorithms.Subjects

Randomized Algorithms | algorithms | efficient in time and space | randomization | computational problems | data structures | graph algorithms | optimization | geometry | Markov chains | sampling | estimation | geometric algorithms | parallel and distributed algorithms | parallel and ditributed algorithm | parallel and distributed algorithm | random sampling | random selection of witnesses | symmetry breaking | randomized computational models | hash tables | skip lists | minimum spanning trees | shortest paths | minimum cuts | convex hulls | linear programming | fixed dimension | arbitrary dimension | approximate counting | parallel algorithms | online algorithms | derandomization techniques | probabilistic analysis | computational number theory | simplicity | speed | design | basic probability theory | application | randomized complexity classes | game-theoretic techniques | Chebyshev | moment inequalities | limited independence | coupon collection | occupancy problems | tail inequalities | Chernoff bound | conditional expectation | probabilistic method | random walks | algebraic techniques | probability amplification | sorting | searching | combinatorial optimization | approximation | counting problems | distributed algorithms | 6.856 | 18.416License

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 metadata14.11 Putting Social Sciences to the Test: Field Experiments in Economics (MIT)

Description

14.11 is a new class on the topic of field (that is, 'in situ') and laboratory experiments in the social sciences - both what these experiments have taught and can teach us and how to conduct them.Subjects

racial discrimination | public health and persuasion | incentives | gender differences in economic environments | intrinsic motivation and fairness | educational quality | corruption | learning and social effects | housing experiments | voting behavior and political economy | jury advocacy | causal inference | internal and external threats | clustering | standard errors | randomization | statistical inference with multiple outcomesLicense

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 metadata6.854J Advanced Algorithms (MIT)

Description

This course is a first-year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms. Domains include string algorithms, network optimization, parallel algorithms, computational geometry, online algorithms, external memory, cache, and streaming algorithms, and data structures.Subjects

amortization | randomization | fingerprinting | word-level parallelism | bit scaling | dynamic programming | network flow | linear programming | fixed-parameter algorithms | approximation algorithms | string algorithms | network optimization | parallel algorithms | computational geometry | online algorithms | external memory | external cache | external streaming | data structuresLicense

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 metadata14.11 Putting Social Sciences to the Test: Field Experiments in Economics (MIT)

Description

14.11 is a new class on the topic of field (that is, 'in situ') and laboratory experiments in the social sciences - both what these experiments have taught and can teach us and how to conduct them.Subjects

racial discrimination | public health and persuasion | incentives | gender differences in economic environments | intrinsic motivation and fairness | educational quality | corruption | learning and social effects | housing experiments | voting behavior and political economy | jury advocacy | causal inference | internal and external threats | clustering | standard errors | randomization | statistical inference with multiple outcomesLicense

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 metadata18.433 Combinatorial Optimization (MIT)

Description

Combinatorial Optimization provides a thorough treatment of linear programming and combinatorial optimization. Topics include network flow, matching theory, matroid optimization, and approximation algorithms for NP-hard problems.Subjects

linear programming | combinatorial optimization | network flow | matching theory | matroid optimization | approximation algorithms for NP-hard problems | approximation algorithms | NP-hard problems | discrete mathematics | fundamental algorithmic techniques | convex programming | flow theory | randomizationLicense

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 metadata6.854J Advanced Algorithms (MIT)

Description

This course is a first-year graduate course in algorithms. Emphasis is placed on fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Techniques to be covered include amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms. Domains include string algorithms, network optimization, parallel algorithms, computational geometry, online algorithms, external memory, cache, and streaming algorithms, and data structures.Subjects

amortization | randomization | fingerprinting | word-level parallelism | bit scaling | dynamic programming | network flow | linear programming | fixed-parameter algorithms | approximation algorithms | string algorithms | network optimization | parallel algorithms | computational geometry | online algorithms | external memory | external cache | external streaming | data structuresLicense

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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