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15.099 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

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 metadata2.717J Optical Engineering (MIT) 2.717J Optical Engineering (MIT)

Description

This course concerns the theory and practice of optical methods in engineering and system design, with an emphasis on diffraction, statistical optics, holography, and imaging. It provides the engineering methodology skills necessary to incorporate optical components in systems serving diverse areas such as precision engineering and metrology, bio-imaging, and computing (sensors, data storage, communication in multi-processor systems). Experimental demonstrations and a design project are included. This course concerns the theory and practice of optical methods in engineering and system design, with an emphasis on diffraction, statistical optics, holography, and imaging. It provides the engineering methodology skills necessary to incorporate optical components in systems serving diverse areas such as precision engineering and metrology, bio-imaging, and computing (sensors, data storage, communication in multi-processor systems). Experimental demonstrations and a design project are included.Subjects

optical methods in engineering and system design | optical methods in engineering and system design | diffraction | statistical optics | holography | and imaging | diffraction | statistical optics | holography | and imaging | Statistical Optics | Inverse Problems (i.e. theory of imaging) | Statistical Optics | Inverse Problems (i.e. theory of imaging) | applications in precision engineering and metrology | bio-imaging | and computing (sensors | data storage | communication in multi-processor systems) | applications in precision engineering and metrology | bio-imaging | and computing (sensors | data storage | communication in multi-processor systems) | Fourier optics | Fourier optics | probability | probability | stochastic processes | stochastic processes | light statistics | light statistics | theory of light coherence | theory of light coherence | van Cittert-Zernicke Theorem | van Cittert-Zernicke Theorem | statistical optics applications | statistical optics applications | inverse problems | inverse problems | information-theoretic views | information-theoretic views | information theory | information theory | 2.717 | 2.717 | MAS.857 | MAS.857License

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 is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection. This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection.Subjects

Input-output | Input-output | state-space models | state-space models | linear systems | linear systems | deterministic and random signals | deterministic and random signals | time- and transform-domain representations | time- and transform-domain representations | sampling | sampling | discrete-time processing | discrete-time processing | continuous-time signals | continuous-time signals | state feedback | state feedback | observers | observers | probabilistic models | probabilistic models | stochastic processes | stochastic processes | correlation functions | correlation functions | power spectra | power spectra | whitening filters | whitening filters | Detection | Detection | matched filters | matched filters | Least-mean square error estimation | Least-mean square error estimation | Wiener filtering | Wiener filteringLicense

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 seminar is intended for doctoral students and discusses topics in applied probability. This semester includes a variety of fields, namely statistical physics (local weak convergence and correlation decay), artificial intelligence (belief propagation algorithms), computer science (random K-SAT problem, coloring, average case complexity) and electrical engineering (low density parity check (LDPC) codes). This seminar is intended for doctoral students and discusses topics in applied probability. This semester includes a variety of fields, namely statistical physics (local weak convergence and correlation decay), artificial intelligence (belief propagation algorithms), computer science (random K-SAT problem, coloring, average case complexity) and electrical engineering (low density parity check (LDPC) codes).Subjects

doctoral | doctoral | seminar | seminar | applied probability | applied probability | stochastic processes | stochastic processes | statistical physics | statistical physics | artificial intelligence | artificial intelligence | computer science | computer science | belief propagation algorithms | belief propagation algorithms | K-SAT problem | K-SAT problem | coloring | coloring | average case complexity | average case complexity | low density parity check codes | low density parity check codesLicense

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 metadata18.466 Mathematical Statistics (MIT) 18.466 Mathematical Statistics (MIT)

Description

This graduate level mathematics course covers decision theory, estimation, confidence intervals, and hypothesis testing. The course also introduces students to large sample theory. Other topics covered include asymptotic efficiency of estimates, exponential families, and sequential analysis. This graduate level mathematics course covers decision theory, estimation, confidence intervals, and hypothesis testing. The course also introduces students to large sample theory. Other topics covered include asymptotic efficiency of estimates, exponential families, and sequential analysis.Subjects

decision theory | decision theory | testing simple hypothesis | testing simple hypothesis | Neyman-Pearson Lemma | Neyman-Pearson Lemma | Bayes decision theory | Bayes decision theory | sufficiency | sufficiency | estimation | estimation | convexity | convexity | Lehmann-Scheff'e property | Lehmann-Scheff'e property | Exponential families | Exponential families | Stein?s phenomenon | Stein?s phenomenon | James-Stein estimators | James-Stein estimators | M-estimation | M-estimation | Solari?s example | Solari?s example | Wilks?s theorem | Wilks?s theorem | stochastic processes | stochastic processes | probability theory | probability theory | confidence intervals | confidence intervals | large sample theory | large sample theory | sequential analysis | sequential analysis | asymptotic efficiency | asymptotic efficiencyLicense

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 metadata15.070J Advanced Stochastic Processes (MIT) 15.070J Advanced Stochastic Processes (MIT)

Description

This class covers the analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems, elements of large deviations theory, Brownian motion and reflected Brownian motion, stochastic integration and Ito calculus and functional limit theorems. In addition, the class will go over some applications to finance theory, insurance, queueing and inventory models. This class covers the analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems, elements of large deviations theory, Brownian motion and reflected Brownian motion, stochastic integration and Ito calculus and functional limit theorems. In addition, the class will go over some applications to finance theory, insurance, queueing and inventory models.Subjects

analysis | analysis | modeling | modeling | stochastic processes | stochastic processes | theoretic probability | theoretic probability | martingales | martingales | filtration | filtration | stopping theorems | stopping theorems | large deviations theory | large deviations theory | Brownian motion | Brownian motion | reflected Brownian motion | reflected Brownian motion | stochastic integration | stochastic integration | Ito calculus | Ito calculus | functional limit theorems | functional limit theorems | applications | applications | finance theory | finance theory | insurance | insurance | queueing | queueing | inventory models | inventory modelsLicense

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 metadata15.070 Advanced Stochastic Processes (MIT) 15.070 Advanced Stochastic Processes (MIT)

Description

The class covers the analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems, elements of large deviations theory, Brownian motion and reflected Brownian motion, stochastic integration and Ito calculus and functional limit theorems. In addition, the class will go over some applications to finance theory, insurance, queueing and inventory models. The class covers the analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems, elements of large deviations theory, Brownian motion and reflected Brownian motion, stochastic integration and Ito calculus and functional limit theorems. In addition, the class will go over some applications to finance theory, insurance, queueing and inventory models.Subjects

analysis | analysis | modeling | modeling | stochastic processes | stochastic processes | theoretic probability | theoretic probability | martingales | martingales | filtration | filtration | stopping theorems | stopping theorems | large deviations theory | large deviations theory | Brownian motion | Brownian motion | reflected Brownian motion | reflected Brownian motion | stochastic integration | stochastic integration | Ito calculus | Ito calculus | functional limit theorems | functional limit theorems | applications | applications | finance theory | finance theory | insurance | insurance | queueing | queueing | inventory models | inventory modelsLicense

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 metadata15.099 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.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 | approximationLicense

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 metadata15.070 Advanced Stochastic Processes (MIT)

Description

The class covers the analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems, elements of large deviations theory, Brownian motion and reflected Brownian motion, stochastic integration and Ito calculus and functional limit theorems. In addition, the class will go over some applications to finance theory, insurance, queueing and inventory models.Subjects

analysis | modeling | stochastic processes | theoretic probability | martingales | filtration | stopping theorems | large deviations theory | Brownian motion | reflected Brownian motion | stochastic integration | Ito calculus | functional limit theorems | applications | finance theory | insurance | queueing | inventory modelsLicense

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 metadata6.011 Introduction to Communication, Control, and Signal Processing (MIT)

Description

This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection.Subjects

Input-output | state-space models | linear systems | deterministic and random signals | time- and transform-domain representations | sampling | discrete-time processing | continuous-time signals | state feedback | observers | probabilistic models | stochastic processes | correlation functions | power spectra | whitening filters | Detection | matched filters | Least-mean square error estimation | Wiener filteringLicense

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 metadata2.717J Optical Engineering (MIT)

Description

This course concerns the theory and practice of optical methods in engineering and system design, with an emphasis on diffraction, statistical optics, holography, and imaging. It provides the engineering methodology skills necessary to incorporate optical components in systems serving diverse areas such as precision engineering and metrology, bio-imaging, and computing (sensors, data storage, communication in multi-processor systems). Experimental demonstrations and a design project are included.Subjects

optical methods in engineering and system design | diffraction | statistical optics | holography | and imaging | Statistical Optics | Inverse Problems (i.e. theory of imaging) | applications in precision engineering and metrology | bio-imaging | and computing (sensors | data storage | communication in multi-processor systems) | Fourier optics | probability | stochastic processes | light statistics | theory of light coherence | van Cittert-Zernicke Theorem | statistical optics applications | inverse problems | information-theoretic views | information theory | 2.717 | MAS.857License

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 metadata15.070 Advanced Stochastic Processes (MIT)

Description

The class covers the analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems, elements of large deviations theory, Brownian motion and reflected Brownian motion, stochastic integration and Ito calculus and functional limit theorems. In addition, the class will go over some applications to finance theory, insurance, queueing and inventory models.Subjects

analysis | modeling | stochastic processes | theoretic probability | martingales | filtration | stopping theorems | large deviations theory | Brownian motion | reflected Brownian motion | stochastic integration | Ito calculus | functional limit theorems | applications | finance theory | insurance | queueing | inventory modelsLicense

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 metadata2.717J Optical Engineering (MIT)

Description

This course concerns the theory and practice of optical methods in engineering and system design, with an emphasis on diffraction, statistical optics, holography, and imaging. It provides the engineering methodology skills necessary to incorporate optical components in systems serving diverse areas such as precision engineering and metrology, bio-imaging, and computing (sensors, data storage, communication in multi-processor systems). Experimental demonstrations and a design project are included.Subjects

optical methods in engineering and system design | diffraction | statistical optics | holography | and imaging | Statistical Optics | Inverse Problems (i.e. theory of imaging) | applications in precision engineering and metrology | bio-imaging | and computing (sensors | data storage | communication in multi-processor systems) | Fourier optics | probability | stochastic processes | light statistics | theory of light coherence | van Cittert-Zernicke Theorem | statistical optics applications | inverse problems | information-theoretic views | information theory | 2.717 | MAS.857License

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 metadata15.070J Advanced Stochastic Processes (MIT)

Description

This class covers the analysis and modeling of stochastic processes. Topics include measure theoretic probability, martingales, filtration, and stopping theorems, elements of large deviations theory, Brownian motion and reflected Brownian motion, stochastic integration and Ito calculus and functional limit theorems. In addition, the class will go over some applications to finance theory, insurance, queueing and inventory models.Subjects

analysis | modeling | stochastic processes | theoretic probability | martingales | filtration | stopping theorems | large deviations theory | Brownian motion | reflected Brownian motion | stochastic integration | Ito calculus | functional limit theorems | applications | finance theory | insurance | queueing | inventory modelsLicense

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 metadata15.098 Special Seminar in Applied Probability and Stochastic Processes (MIT)

Description

This seminar is intended for doctoral students and discusses topics in applied probability. This semester includes a variety of fields, namely statistical physics (local weak convergence and correlation decay), artificial intelligence (belief propagation algorithms), computer science (random K-SAT problem, coloring, average case complexity) and electrical engineering (low density parity check (LDPC) codes).Subjects

doctoral | seminar | applied probability | stochastic processes | statistical physics | artificial intelligence | computer science | belief propagation algorithms | K-SAT problem | coloring | average case complexity | low density parity check codesLicense

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 metadata6.011 Introduction to Communication, Control, and Signal Processing (MIT)

Description

This course is taken mainly by undergraduates, and explores ideas involving signals, systems and probabilistic models in the context of communication, control and signal processing applications. The material expands out from the basics in 6.003 and 6.041. The treatment involves aspects of analysis, synthesis, and optimization. Topics covered differ somewhat from semester to semester, but typically include: random processes, correlations, spectral densities, state-space modeling, multirate processing, signal estimation and detection.Subjects

Input-output | state-space models | linear systems | deterministic and random signals | time- and transform-domain representations | sampling | discrete-time processing | continuous-time signals | state feedback | observers | probabilistic models | stochastic processes | correlation functions | power spectra | whitening filters | Detection | matched filters | Least-mean square error estimation | Wiener filteringLicense

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 metadata18.466 Mathematical Statistics (MIT)

Description

This graduate level mathematics course covers decision theory, estimation, confidence intervals, and hypothesis testing. The course also introduces students to large sample theory. Other topics covered include asymptotic efficiency of estimates, exponential families, and sequential analysis.Subjects

decision theory | testing simple hypothesis | Neyman-Pearson Lemma | Bayes decision theory | sufficiency | estimation | convexity | Lehmann-Scheff'e property | Exponential families | Stein?s phenomenon | James-Stein estimators | M-estimation | Solari?s example | Wilks?s theorem | stochastic processes | probability theory | confidence intervals | large sample theory | sequential analysis | asymptotic efficiencyLicense

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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