CME 364A: Convex Optimization I (CS 334A, EE 364A)
Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Leastsquares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interiorpoint methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as
EE263, basic probability.
Terms: Win, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Boyd, S. (PI)
;
Busseti, E. (TA)
;
Dao Phuc Quang, T. (TA)
;
Fannjiang, C. (TA)
...
more instructors for CME 364A »
Instructors:
Boyd, S. (PI)
;
Busseti, E. (TA)
;
Dao Phuc Quang, T. (TA)
;
Fannjiang, C. (TA)
;
Go, K. (TA)
;
Hong, J. (TA)
;
Khosravi, K. (TA)
;
Lawson, D. (TA)
;
Lemon, A. (TA)
;
Li, C. (TA)
;
Zhechev, Z. (TA)
CS 334A: Convex Optimization I (CME 364A, EE 364A)
Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Leastsquares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interiorpoint methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as
EE263, basic probability.
Terms: Win, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Boyd, S. (PI)
;
Busseti, E. (TA)
;
Dao Phuc Quang, T. (TA)
;
Fannjiang, C. (TA)
...
more instructors for CS 334A »
Instructors:
Boyd, S. (PI)
;
Busseti, E. (TA)
;
Dao Phuc Quang, T. (TA)
;
Fannjiang, C. (TA)
;
Go, K. (TA)
;
Hong, J. (TA)
;
Khosravi, K. (TA)
;
Lawson, D. (TA)
;
Lemon, A. (TA)
;
Li, C. (TA)
;
Zhechev, Z. (TA)
EE 257: Applied Optimization Laboratory (Geophys 258) (GEOPHYS 258)
Application of optimization and estimation methods to the analysis and modeling of large observational data sets. Laboratory exercises using inverse theory and applied linear algebra to solve problems of indirect and noisy measurements. Emphasis on practical solution of scientific and engineering problems, especially those requiring large amounts of data, on digital computers using scientific languages. Also addresses advantages of largescale computing, including hardware architectures, input/output and data bus bandwidth, programming efficiency, parallel programming techniques. Student projects involve analyzing real data by implementing observational systems such as tomography for medical and Earth observation uses, radar and matched filtering, multispectral/multitemporal studies, or migration processing. Prequisites: Programming with high level language. Recommended:
EE261,
EE263,
EE178, ME300 or equivalent.
Terms: not given this year

Units: 34

Grading: Letter or Credit/No Credit
EE 263: Introduction to Linear Dynamical Systems (CME 263)
Applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Topics: leastsquares approximations of overdetermined equations, and leastnorm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singularvalue decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multiinput/multioutput systems, impulse and step matrices; convolution and transfermatrix descriptions. Control, reachability, and state transfer; observability and leastsquares state estimation. Prerequisites: linear algebra and matrices as in
MATH104; differential equations and Laplace transforms as in
EE102B.
Terms: Aut, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Lall, S. (PI)
EE 364A: Convex Optimization I (CME 364A, CS 334A)
Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Leastsquares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interiorpoint methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as
EE263, basic probability.
Terms: Win, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Boyd, S. (PI)
;
Busseti, E. (TA)
;
Dao Phuc Quang, T. (TA)
;
Fannjiang, C. (TA)
...
more instructors for EE 364A »
Instructors:
Boyd, S. (PI)
;
Busseti, E. (TA)
;
Dao Phuc Quang, T. (TA)
;
Fannjiang, C. (TA)
;
Go, K. (TA)
;
Hong, J. (TA)
;
Khosravi, K. (TA)
;
Lawson, D. (TA)
;
Lemon, A. (TA)
;
Li, C. (TA)
;
Zhechev, Z. (TA)
EE 373A: Adaptive Signal Processing
Learning algorithms for adaptive digital filters. Selfoptimization. Wiener filter theory. Quadratic performance functions, their eigenvectors and eigenvalues. Speed of convergence. Asymptotic performance versus convergence rate. Applications of adaptive filters to statistical prediction, process modeling, adaptive noise canceling, adaptive antenna arrays, adaptive inverse control, and equalization and echo canceling in modems. Artificial neural networks. Cognitive memory/human and machine. Natural and artificial synapses. Hebbian learning. The HebbianLMS algorithm. Theoretical and experimental research projects in adaptive filter theory, communications, audio systems, and neural networks. Biomedical research projects, supervised jointly by EE and Medical School faculty. Recommended:
EE263,
EE264,
EE278.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Widrow, B. (PI)
;
White, N. (TA)
EE 378B: Inference, Estimation, and Information Processing
Techniques and models for signal, data and information processing, with emphasis on incomplete data, nonordered index sets and robust lowcomplexity methods. Linear models; regularization and shrinkage; dimensionality reduction; streaming algorithms; sketching; clustering, search in high dimension; lowrank models; principal component analysis.nnApplications include: positioning from pairwise distances; distributed sensing; measurement/traffic monitoring in networks; finding communities/clusters in networks; recommendation systems; inverse problems. Prerequisites: EE278 and EE263 or equivalent. Recommended but not required:
EE378A
Terms: not given this year

Units: 3

Grading: Letter or Credit/No Credit
GEOPHYS 258: Applied Optimization Laboratory (Geophys 258) (EE 257)
Application of optimization and estimation methods to the analysis and modeling of large observational data sets. Laboratory exercises using inverse theory and applied linear algebra to solve problems of indirect and noisy measurements. Emphasis on practical solution of scientific and engineering problems, especially those requiring large amounts of data, on digital computers using scientific languages. Also addresses advantages of largescale computing, including hardware architectures, input/output and data bus bandwidth, programming efficiency, parallel programming techniques. Student projects involve analyzing real data by implementing observational systems such as tomography for medical and Earth observation uses, radar and matched filtering, multispectral/multitemporal studies, or migration processing. Prequisites: Programming with high level language. Recommended:
EE261,
EE263,
EE178, ME300 or equivalent.
Terms: not given this year

Units: 34

Grading: Letter or Credit/No Credit
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