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16.323 Principles of Optimal Control (MIT) 16.323 Principles of Optimal Control (MIT)

Description

This course studies the principles of deterministic optimal control. It uses variational calculus and Pontryagin's maximum principle. It focuses on applications of the theory, including optimal feedback control, time-optimal control, and others. Dynamic programming and numerical search algorithms are introduced briefly. This course studies the principles of deterministic optimal control. It uses variational calculus and Pontryagin's maximum principle. It focuses on applications of the theory, including optimal feedback control, time-optimal control, and others. Dynamic programming and numerical search algorithms are introduced briefly.Subjects

nonlinear optimization | nonlinear optimization | linear quadratic regulators | linear quadratic regulators | MATLAB implementation | MATLAB implementation | dynamic programming | dynamic programming | calculus of variations | calculus of variations | LQR | LQR | LQG | LQG | stochastic optimization | stochastic optimization | on-line optimization and control | on-line optimization and control | constrained optimization | constrained optimization | signals | signals | system norms | system norms | Model Predictive Behavior | Model Predictive Behavior | quadratic programming | quadratic programming | mixed-integer linear programming | mixed-integer linear programming | linear programming | linear programmingLicense

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See all metadata15.053 Introduction to Optimization (MIT) 15.053 Introduction to Optimization (MIT)

Description

15.053 is an undergraduate subject in the theory and practice of optimization. We will consider optimization models with applications to transportation, logistics, manufacturing, computer science, E-business, project management, finance as well as several other domains. This subject will survey some of the applications of optimization as well as heuristics, and we will present algorithms and theory for linear programming, dynamic programming, integer programming, and non-linear programming.One way of summarizing a subject is a lecture by lecture description of the subject, or a description of the methodologies presented in the subject. We do list a lecture by lecture description, but first we describe several cross cutting themes. 15.053 is an undergraduate subject in the theory and practice of optimization. We will consider optimization models with applications to transportation, logistics, manufacturing, computer science, E-business, project management, finance as well as several other domains. This subject will survey some of the applications of optimization as well as heuristics, and we will present algorithms and theory for linear programming, dynamic programming, integer programming, and non-linear programming.One way of summarizing a subject is a lecture by lecture description of the subject, or a description of the methodologies presented in the subject. We do list a lecture by lecture description, but first we describe several cross cutting themes.Subjects

finance | finance | project management | project management | E-commerce | E-commerce | heuristics | heuristics | non-linear programming | non-linear programming | integer programming | integer programming | dynamic programming | dynamic programming | network optimization | network optimization | linear programming | linear programmingLicense

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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Includes audio/video content: AV lectures. This graduate-level course is a continuation of Mathematical Methods for Engineers I (18.085). Topics include numerical methods; initial-value problems; network flows; and optimization. Includes audio/video content: AV lectures. This graduate-level course is a continuation of Mathematical Methods for Engineers I (18.085). Topics include numerical methods; initial-value problems; network flows; and optimization.Subjects

Scientific computing: Fast Fourier Transform | Scientific computing: Fast Fourier Transform | finite differences | finite differences | finite elements | finite elements | spectral method | spectral method | numerical linear algebra | numerical linear algebra | Complex variables and applications | Complex variables and applications | Initial-value problems: stability or chaos in ordinary differential equations | Initial-value problems: stability or chaos in ordinary differential equations | wave equation versus heat equation | wave equation versus heat equation | conservation laws and shocks | conservation laws and shocks | dissipation and dispersion | dissipation and dispersion | Optimization: network flows | Optimization: network flows | linear programming | linear programming | Scientific computing: Fast Fourier Transform | finite differences | finite elements | spectral method | numerical linear algebra | Scientific computing: Fast Fourier Transform | finite differences | finite elements | spectral method | numerical linear algebra | Initial-value problems: stability or chaos in ordinary differential equations | wave equation versus heat equation | conservation laws and shocks | dissipation and dispersion | Initial-value problems: stability or chaos in ordinary differential equations | wave equation versus heat equation | conservation laws and shocks | dissipation and dispersion | Optimization: network flows | linear programming | Optimization: network flows | linear programmingLicense

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See all metadata6.252J Nonlinear Programming (MIT) 6.252J Nonlinear Programming (MIT)

Description

6.252J is a course in the department's "Communication, Control, and Signal Processing" concentration. This course provides a unified analytical and computational approach to nonlinear optimization problems. The topics covered in this course include: unconstrained optimization methods, constrained optimization methods, convex analysis, Lagrangian relaxation, nondifferentiable optimization, and applications in integer programming. There is also a comprehensive treatment of optimality conditions, Lagrange multiplier theory, and duality theory. Throughout the course, applications are drawn from control, communications, power systems, and resource allocation problems. 6.252J is a course in the department's "Communication, Control, and Signal Processing" concentration. This course provides a unified analytical and computational approach to nonlinear optimization problems. The topics covered in this course include: unconstrained optimization methods, constrained optimization methods, convex analysis, Lagrangian relaxation, nondifferentiable optimization, and applications in integer programming. There is also a comprehensive treatment of optimality conditions, Lagrange multiplier theory, and duality theory. Throughout the course, applications are drawn from control, communications, power systems, and resource allocation problems.Subjects

nonlinear programming | nonlinear programming | non-linear programming | non-linear programming | nonlinear optimization | nonlinear optimization | unconstrained optimization | unconstrained optimization | gradient | gradient | conjugate direction | conjugate direction | Newton | Newton | quasi-Newton methods | quasi-Newton methods | constrained optimization | constrained optimization | feasible directions | feasible directions | projection | projection | interior point | interior point | Lagrange multiplier | Lagrange multiplier | convex analysis | convex analysis | Lagrangian relaxation | Lagrangian relaxation | nondifferentiable optimization | nondifferentiable optimization | integer programming | integer programming | optimality conditions | optimality conditions | Lagrange multiplier theory | Lagrange multiplier theory | duality theory | duality theory | control | control | communications | communications | power systems | power systems | resource allocation | resource allocation | 6.252 | 6.252 | 15.084 | 15.084License

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See all metadata15.099 Readings in Optimization (MIT) 15.099 Readings in Optimization (MIT)

Description

In keeping with the tradition of the last twenty-some years, the Readings in Optimization seminar will focus on an advanced topic of interest to a portion of the MIT optimization community: randomized methods for deterministic optimization. In contrast to conventional optimization algorithms whose iterates are computed and analyzed deterministically, randomized methods rely on stochastic processes and random number/vector generation as part of the algorithm and/or its analysis. In the seminar, we will study some very recent papers on this topic, many by MIT faculty, as well as some older papers from the existing literature that are only now receiving attention. In keeping with the tradition of the last twenty-some years, the Readings in Optimization seminar will focus on an advanced topic of interest to a portion of the MIT optimization community: randomized methods for deterministic optimization. In contrast to conventional optimization algorithms whose iterates are computed and analyzed deterministically, randomized methods rely on stochastic processes and random number/vector generation as part of the algorithm and/or its analysis. In the seminar, we will study some very recent papers on this topic, many by MIT faculty, as well as some older papers from the existing literature that are only now receiving attention.Subjects

deterministic optimization; algorithms; stochastic processes; random number generation; simplex method; nonlinear; convex; complexity analysis; semidefinite programming; heuristic; global optimization; Las Vegas algorithm; randomized algorithm; linear programming; search techniques; hit and run; NP-hard; approximation | deterministic optimization; algorithms; stochastic processes; random number generation; simplex method; nonlinear; convex; complexity analysis; semidefinite programming; heuristic; global optimization; Las Vegas algorithm; randomized algorithm; linear programming; search techniques; hit and run; NP-hard; approximation | deterministic optimization | deterministic optimization | algorithms | algorithms | stochastic processes | stochastic processes | random number generation | random number generation | simplex method | simplex method | nonlinear | nonlinear | convex | convex | complexity analysis | complexity analysis | semidefinite programming | semidefinite programming | heuristic | heuristic | global optimization | global optimization | Las Vegas algorithm | Las Vegas algorithm | randomized algorithm | randomized algorithm | linear programming | linear programming | search techniques | search techniques | hit and run | hit and run | NP-hard | NP-hard | approximation | approximationLicense

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One objective of 15.066J is to introduce modeling, optimization and simulation, as it applies to the study and analysis of manufacturing systems for decision support. The introduction of optimization models and algorithms provide a framework to think about a wide range of issues that arise in manufacturing systems. The second objective is to expose students to a wide range of applications for these methods and models, and to integrate this material with their introduction to operations management. One objective of 15.066J is to introduce modeling, optimization and simulation, as it applies to the study and analysis of manufacturing systems for decision support. The introduction of optimization models and algorithms provide a framework to think about a wide range of issues that arise in manufacturing systems. The second objective is to expose students to a wide range of applications for these methods and models, and to integrate this material with their introduction to operations management.Subjects

modeling | modeling | optimization | optimization | simulation | simulation | manufacturing systems | manufacturing systems | decision making | decision making | decision support | decision support | probabilistic simulation | probabilistic simulation | designing manufacturing systems | designing manufacturing systems | operations management | operations management | linear programming | linear programming | sensitivity analysis | sensitivity analysis | network flow problems | network flow problems | non-linear programming | non-linear programming | Lagrange multipliers | Lagrange multipliers | integer programming | integer programming | discrete-event simulation | discrete-event simulation | heuristics | heuristics | algorithms | algorithms | 15.066 | 15.066 | 2.851 | 2.851 | 3.83 | 3.83License

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See all metadata2.854 Manufacturing Systems I (SMA 6304) (MIT) 2.854 Manufacturing Systems I (SMA 6304) (MIT)

Description

As the first in a sequence of four half-term courses, this course will provide the fundamental building blocks for conceptualizing, understanding and optimizing manufacturing systems and supply chains. These building blocks include process analysis, queuing theory, simulation, forecasting, inventory theory and linear programming. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 6304 (Manufacturing Systems I: Analytical Methods and Flow Models). As the first in a sequence of four half-term courses, this course will provide the fundamental building blocks for conceptualizing, understanding and optimizing manufacturing systems and supply chains. These building blocks include process analysis, queuing theory, simulation, forecasting, inventory theory and linear programming. This course was also taught as part of the Singapore-MIT Alliance (SMA) programme as course number SMA 6304 (Manufacturing Systems I: Analytical Methods and Flow Models).Subjects

conceptualizing | conceptualizing | understanding and optimizing manufacturing systems and supply chains | understanding and optimizing manufacturing systems and supply chains | process analysis | process analysis | queueing theory | queueing theory | simulation | simulation | forecasting | forecasting | inventory theory | inventory theory | linear programming | linear programming | conceptualizing | understanding and optimizing manufacturing systems and supply chains | conceptualizing | understanding and optimizing manufacturing systems and supply chains | SMA 6304 | SMA 6304License

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See all metadata15.082J Network Optimization (MIT) 15.082J Network Optimization (MIT)

Description

15.082J/6.855J is an H-level graduate subject in the theory and practice of network flows and its extensions. Network flow problems form a subclass of linear programming problems with applications to transportation, logistics, manufacturing, computer science, project management, finance as well as a number of other domains. This subject will survey some of the applications of network flows and focus on key special cases of network flow problems including the following: the shortest path problem, the maximum flow problem, the minimum cost flow problem, and the multi-commodity flow problem. 15.082J/6.855J is an H-level graduate subject in the theory and practice of network flows and its extensions. Network flow problems form a subclass of linear programming problems with applications to transportation, logistics, manufacturing, computer science, project management, finance as well as a number of other domains. This subject will survey some of the applications of network flows and focus on key special cases of network flow problems including the following: the shortest path problem, the maximum flow problem, the minimum cost flow problem, and the multi-commodity flow problem.Subjects

network flows | network flows | extensions | extensions | network flow problems | network flow problems | transportation | transportation | logistics | logistics | manufacturing | manufacturing | computer science | computer science | project management | project management | finance | finance | the shortest path problem | the shortest path problem | the maximum flow problem | the maximum flow problem | the minimum cost flow problem | the minimum cost flow problem | the multi-commodity flow problem | the multi-commodity flow problem | communication | communication | systems | systems | applications | applications | efficiency | efficiency | algorithms | algorithms | traffic | traffic | equilibrium | equilibrium | design | design | mplementation | mplementation | linear programming | linear programming | implementation | implementation | computer | computer | science | science | linear | linear | programming | programming | network | network | flow | flow | problems | problems | project | project | management | management | maximum | maximum | minimum | minimum | cost | cost | multi-commodity | multi-commodity | shortest | shortest | path | path | 15.082 | 15.082 | 6.855 | 6.855License

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This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace informationSubjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search treesLicense

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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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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See all metadata18.310 Principles of Applied Mathematics (MIT) 18.310 Principles of Applied Mathematics (MIT)

Description

Principles of Applied Mathematics is a study of illustrative topics in discrete applied mathematics including sorting algorithms, information theory, coding theory, secret codes, generating functions, linear programming, game theory. There is an emphasis on topics that have direct application in the real world. Principles of Applied Mathematics is a study of illustrative topics in discrete applied mathematics including sorting algorithms, information theory, coding theory, secret codes, generating functions, linear programming, game theory. There is an emphasis on topics that have direct application in the real world.Subjects

sorting algorithms | sorting algorithms | information theory | information theory | coding theory | coding theory | secret codes | secret codes | generating functions | generating functions | linear programming | linear programming | game theory | game theoryLicense

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

Description

6.854J is a first-year graduate course in algorithms, continuing where 6.046J left off. The course emphasizes fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Topics include: data structures, network flows, linear programming, computational geometry, approximation algorithms. 6.854J is a first-year graduate course in algorithms, continuing where 6.046J left off. The course emphasizes fundamental algorithms and advanced methods of algorithmic design, analysis, and implementation. Topics include: data structures, network flows, linear programming, computational geometry, approximation algorithms.Subjects

algorithm design and analysis | algorithm design and analysis | algorithms | algorithms | fundamental algorithms | fundamental algorithms | advanced methods of algorithmic design | advanced methods of algorithmic design | analysis | analysis | implementation | implementation | data structures | data structures | network flows | network flows | linear programming | linear programming | computational geometry | computational geometry | approximation algorithms | approximation algorithms | algorithmic design | algorithmic design | algorithmic analysis | algorithmic analysis | string algorithms | string algorithms | maximum flows | maximum flows | online algorithms | online algorithms | scheduling | scheduling | external memory algorithms | external memory algorithms | 6.854 | 6.854 | 18.415 | 18.415License

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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This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace information This course surveys a variety of reasoning, optimization, and decision-making methodologies for creating highly autonomous systems and decision support aids. The focus is on principles, algorithms, and their applications, taken from the disciplines of artificial intelligence and operations research. Reasoning paradigms include logic and deduction, heuristic and constraint-based search, model-based reasoning, planning and execution, reasoning under uncertainty, and machine learning. Optimization paradigms include linear, integer and dynamic programming. Decision-making paradigms include decision theoretic planning, and Markov decision processes. This course is offered both to undergraduate (16.410) students as a professional area undergraduate subject, in the field of aerospace informationSubjects

autonomy | autonomy | decision | decision | decision-making | decision-making | reasoning | reasoning | optimization | optimization | autonomous | autonomous | autonomous systems | autonomous systems | decision support | decision support | algorithms | algorithms | artificial intelligence | artificial intelligence | a.i. | a.i. | operations | operations | operations research | operations research | logic | logic | deduction | deduction | heuristic search | heuristic search | constraint-based search | constraint-based search | model-based reasoning | model-based reasoning | planning | planning | execution | execution | uncertainty | uncertainty | machine learning | machine learning | linear programming | linear programming | dynamic programming | dynamic programming | integer programming | integer programming | network optimization | network optimization | decision analysis | decision analysis | decision theoretic planning | decision theoretic planning | Markov decision process | Markov decision process | scheme | scheme | propositional logic | propositional logic | constraints | constraints | Markov processes | Markov processes | computational performance | computational performance | satisfaction | satisfaction | learning algorithms | learning algorithms | system state | system state | state | state | search treees | search treees | plan spaces | plan spaces | model theory | model theory | decision trees | decision trees | function approximators | function approximators | optimization algorithms | optimization algorithms | limitations | limitations | tradeoffs | tradeoffs | search and reasoning | search and reasoning | game tree search | game tree search | local stochastic search | local stochastic search | stochastic | stochastic | genetic algorithms | genetic algorithms | constraint satisfaction | constraint satisfaction | propositional inference | propositional inference | rule-based systems | rule-based systems | rule-based | rule-based | model-based diagnosis | model-based diagnosis | neural nets | neural nets | reinforcement learning | reinforcement learning | web-based | web-based | search trees | search treesLicense

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See all metadata18.310 Principles of Applied Mathematics (MIT) 18.310 Principles of Applied Mathematics (MIT)

Description

Principles of Applied Mathematics is a study of illustrative topics in discrete applied mathematics including sorting algorithms, information theory, coding theory, secret codes, generating functions, linear programming, game theory. There is an emphasis on topics that have direct application in the real world. Principles of Applied Mathematics is a study of illustrative topics in discrete applied mathematics including sorting algorithms, information theory, coding theory, secret codes, generating functions, linear programming, game theory. There is an emphasis on topics that have direct application in the real world.Subjects

sorting algorithms | sorting algorithms | information theory | information theory | coding theory | coding theory | secret codes | secret codes | generating functions | generating functions | linear programming | linear programming | game theory | game theory | discrete applied mathematics | discrete applied mathematics | mathematical analysis | mathematical analysis | sorting data | sorting data | efficient data storage | efficient data storage | efficient data transmission | efficient data transmission | error correction | error correction | secrecy | secrecy | Fast Fourier Transform | Fast Fourier Transform | network-flow problems | network-flow problems | mathematical economics | mathematical economics | statistics | statistics | probability theory | probability theory | combinatorics | combinatorics | linear algebra | linear algebraLicense

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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This graduate-level course is a continuation of Mathematical Methods for Engineers I (18.085). Topics include numerical methods; initial-value problems; network flows; and optimization.Technical RequirementsFile decompression software, such as Winzip® or StuffIt®, is required to open the .zip files found on this course site. MATLAB® software is required to run the .m files found on this course site. This graduate-level course is a continuation of Mathematical Methods for Engineers I (18.085). Topics include numerical methods; initial-value problems; network flows; and optimization.Technical RequirementsFile decompression software, such as Winzip® or StuffIt®, is required to open the .zip files found on this course site. MATLAB® software is required to run the .m files found on this course site.Subjects

Scientific computing: Fast Fourier Transform | Scientific computing: Fast Fourier Transform | finite differences | finite differences | finite elements | finite elements | spectral method | spectral method | numerical linear algebra | numerical linear algebra | Complex variables and applications | Complex variables and applications | Initial-value problems: stability or chaos in ordinary differential equations | Initial-value problems: stability or chaos in ordinary differential equations | wave equation versus heat equation | wave equation versus heat equation | conservation laws and shocks | conservation laws and shocks | dissipation and dispersion | dissipation and dispersion | Optimization: network flows | Optimization: network flows | linear programming | linear programmingLicense

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See all metadata1.224J Carrier Systems (MIT) 1.224J Carrier Systems (MIT)

Description

Carrier systems involve the design, operation and management of transportation networks, assets, personnel, freight and passengers. In this course, we will present models and tools for analyzing, optimizing, planning, managing and controlling carrier systems. Carrier systems involve the design, operation and management of transportation networks, assets, personnel, freight and passengers. In this course, we will present models and tools for analyzing, optimizing, planning, managing and controlling carrier systems.Subjects

carrier systems | carrier systems | design | design | operation | operation | management | management | personnel | personnel | freight and passengers | freight and passengers | models and tools for analyzing | models and tools for analyzing | optimization | optimization | planning | planning | managing and controlling | managing and controlling | transportation networks | transportation networks | assets | assets | freight | freight | passengers | passengers | models | models | tools | tools | analyzing | analyzing | optimizing | optimizing | managing | managing | controlling | controlling | linear programming | linear programming | software | software | integer programming | integer programming | direct transportation | direct transportation | procurement | procurement | transit vehicle scheduling | transit vehicle scheduling | transit crew scheduling | transit crew scheduling | airline routing | airline routing | real-time operations control | real-time operations control | freight transportation | freight transportation | analysis | analysis | plans | plans | control | control | designing | designing | 1.224 | 1.224 | ESD.204 | ESD.204License

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This course is an introduction to the theory and application of large-scale dynamic programming. Topics include Markov decision processes, dynamic programming algorithms, simulation-based algorithms, theory and algorithms for value function approximation, and policy search methods. The course examines games and applications in areas such as dynamic resource allocation, finance and queueing networks. This course is an introduction to the theory and application of large-scale dynamic programming. Topics include Markov decision processes, dynamic programming algorithms, simulation-based algorithms, theory and algorithms for value function approximation, and policy search methods. The course examines games and applications in areas such as dynamic resource allocation, finance and queueing networks.Subjects

algorithm | algorithm | markov decision process | markov decision process | dynamic programming | dynamic programming | stochastic models | stochastic models | policy iteration | policy iteration | Q-Learning | Q-Learning | reinforcement learning | reinforcement learning | Lyapunov function | Lyapunov function | ODE | ODE | TD-Learning | TD-Learning | value function approximation | value function approximation | linear programming | linear programming | policy search | policy search | policy gradient | policy gradient | actor-critic | actor-critic | experts algorithm | experts algorithm | regret minimization and calibration | regret minimization and calibration | games. | games.License

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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

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See all metadata6.856J 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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This subject is on regional energy-environmental modeling rather than on general energy-environmental policies, but the models should have some policy relevance. We will start with some discussion of green accounting issues; then, we will cover a variety of theoretical and empirical topics related to spatial energy demand and supply, energy forecasts, national and regional energy prices, and environmental implications of regional energy consumption and production. Where feasible, the topics will have a spatial dimension. This is a new seminar, so we expect students to contribute material to the set of readings and topics covered during the semester. This subject is on regional energy-environmental modeling rather than on general energy-environmental policies, but the models should have some policy relevance. We will start with some discussion of green accounting issues; then, we will cover a variety of theoretical and empirical topics related to spatial energy demand and supply, energy forecasts, national and regional energy prices, and environmental implications of regional energy consumption and production. Where feasible, the topics will have a spatial dimension. This is a new seminar, so we expect students to contribute material to the set of readings and topics covered during the semester.Subjects

regional energy environmental modeling | regional energy environmental modeling | policies | policies | microeconomics | microeconomics | economic modeling | economic modeling | economic modeling techniques | economic modeling techniques | input-output | input-output | general equilibrium | general equilibrium | linear programming | linear programming | logit | logit | regression | regression | green accounting | green accounting | spatial energy demand | spatial energy demand | spatial energy supply | spatial energy supply | energy forecast | energy forecast | regional energy prices | regional energy prices | regional energy consumption | regional energy consumption | regional energy production | regional energy productionLicense

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 metadata18.310C Principles of Applied Mathematics (MIT) 18.310C Principles of Applied Mathematics (MIT)

Description

Principles of Applied Mathematics is a study of illustrative topics in discrete applied mathematics including sorting algorithms, information theory, coding theory, secret codes, generating functions, linear programming, game theory. There is an emphasis on topics that have direct application in the real world. This course was recently revised to meet the MIT Undergraduate Communication Requirement (CR). It covers the same content as 18.310, but assignments are structured with an additional focus on writing. Principles of Applied Mathematics is a study of illustrative topics in discrete applied mathematics including sorting algorithms, information theory, coding theory, secret codes, generating functions, linear programming, game theory. There is an emphasis on topics that have direct application in the real world. This course was recently revised to meet the MIT Undergraduate Communication Requirement (CR). It covers the same content as 18.310, but assignments are structured with an additional focus on writing.Subjects

sorting algorithms | sorting algorithms | information theory | information theory | coding theory | coding theory | secret codes | secret codes | generating functions | generating functions | linear programming | linear programming | game theory | game theoryLicense

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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)

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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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This course introduces students to the theory, algorithms, and applications of optimization. The optimization methodologies include linear programming, network optimization, integer programming, and decision trees. Applications to logistics, manufacturing, transportation, marketing, project management, and finance. Includes a team project in which students select and solve a problem in practice. This course introduces students to the theory, algorithms, and applications of optimization. The optimization methodologies include linear programming, network optimization, integer programming, and decision trees. Applications to logistics, manufacturing, transportation, marketing, project management, and finance. Includes a team project in which students select and solve a problem in practice.Subjects

optimization methods | optimization methods | management science | management science | theory | theory | algorithms | algorithms | applications | applications | linear programming | linear programming | network optimization | network optimization | integer programming | integer programming | decision trees | decision trees | logistics | logistics | manufacturing | manufacturing | transportation | transportation | marketing | marketing | project management | project management | finance | financeLicense

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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15.053 introduces students to the theory, algorithms, and applications of optimization. Optimization methodologies include linear programming, network optimization, integer programming, decision trees, and dynamic programming. The methods have applications to logistics, manufacturing, transportation, marketing, project management, and finance. 15.053 introduces students to the theory, algorithms, and applications of optimization. Optimization methodologies include linear programming, network optimization, integer programming, decision trees, and dynamic programming. The methods have applications to logistics, manufacturing, transportation, marketing, project management, and finance.Subjects

optimization methods | optimization methods | management science | management science | theory | theory | algorithms | algorithms | applications | applications | linear programming | linear programming | network optimization | network optimization | integer programming | integer programming | decision trees | decision trees | logistics | logistics | manufacturing | manufacturing | transportation | transportation | marketing | marketing | project management | project management | finance | financeLicense

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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