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This course will focus on formulating and solving convex optimization problems arising in engineering and science. Topics include: convex analysis, linear and quadratic programming, semidefinite programming, optimality conditions, duality theory, interior-point methods, subgradient methods, convex relaxation.
<nowiki>*</nowiki> http://www.ics.uci.edu/~xhx/courses/ConvexOpt/introduction.pdf Introduction]
<nowiki>*</nowiki> http://www.ics.uci.edu/~xhx/courses/ConvexOpt/convex_sets.pdf Convex Sets]
<nowiki>*</nowiki> http://www.ics.uci.edu/~xhx/courses/ConvexOpt/convex_functions.pdf Convex Functions]
<nowiki>*</nowiki> http://www.ics.uci.edu/~xhx/courses/ConvexOpt/optimization_problems.pdf Optimization Problems]
<nowiki>*</nowiki> http://www.ics.uci.edu/~xhx/courses/ConvexOpt/optimality_conditions.pdf Optimality Conditions]
<nowiki>*</nowiki> http://www.ics.uci.edu/~xhx/courses/ConvexOpt/duality.pdf Duality]
<nowiki>*</nowiki> http://www.ics.uci.edu/~xhx/courses/ConvexOpt/unconstrained_opt.pdf Unconstrained minimization]
<nowiki>*</nowiki> http://www.ics.uci.edu/~xhx/courses/ConvexOpt/equality.pdf Equality constrained minimization]
<nowiki>*</nowiki> http://www.ics.uci.edu/~xhx/courses/ConvexOpt/interior.pdf Interior-point methods]
<nowiki>*</nowiki> Semi-definite programming http://www.ics.uci.edu/~xhx/courses/ConvexOpt/sdpintro.pdf Introduction to Semidefinite Programming (SDP)] http://www.ics.uci.edu/~xhx/courses/ConvexOpt/semidef_prog.pdf SDP]
<nowiki>*</nowiki> Modeling and application
http://www.ics.uci.edu/~xhx/courses/ConvexOpt/projects/stochastic_subgradient_methods.pdf
Stochastic subgradient methods] <nowiki>*</nowiki> http://www.ics.uci.edu/~xhx/courses/ConvexOpt/projects/stochastic_subgradient_methods_report.pdf more details]
http://www.ics.uci.edu/~xhx/courses/ConvexOpt/projects/Multitask_Feature_Learning.pdf
Multitask feature learning] <nowiki>*</nowiki> http://www.ics.uci.edu/~xhx/courses/ConvexOpt/projects/Multitask_Feature_Learning_report.pdf More details] http://www.ics.uci.edu/~xhx/courses/ConvexOpt/projects/FengJiang.pdf
Beamforming Optimization of MIMO Interference Network] http://www.ics.uci.edu/~xhx/courses/ConvexOpt/projects/color_constancy.pdf Color Constancy] http://www.ics.uci.edu/~xhx/courses/ConvexOpt/projects/approximation.pdf
Approximation and fitting] http://www.ics.uci.edu/~xhx/courses/ConvexOpt/projects/struct_var_detection.pdf Detecting genetic variation using fused Lasso] http://www.ics.uci.edu/~xhx/courses/ConvexOpt/projects/Load_balancing_ConvOpt.pdf
Load balancing on a heterogeneous cluster] http://www.ics.uci.edu/~xhx/courses/ConvexOpt/projects/graph_isomorphism.pdf Detecting graph isomorphism] http://www.ics.uci.edu/~xhx/courses/ConvexOpt/projects/Load_balancing_ConvOpt.pdf
Load balancing on a heterogeneous cluster] http://www.ics.uci.edu/~xhx/courses/ConvexOpt/projects/Modeling_Marketing_Promotion_Choices.pdf Modeling marketing promotion choices]
<nowiki>*</nowiki><nowiki>*</nowiki> http://www.ics.uci.edu/~xhx/courses/ConvexOpt/projects/PatrickFlynn.pdf Conjugate gradient method]
<nowiki>*</nowiki> Final exam: Mar 15, 4:00-6:00pm (Bring one examination blue book!) <nowiki>*</nowiki> Final project due: Mar 18, 5pm, hard copy in Bren Hall 4058
<nowiki>*</nowiki> Convex sets: 2.1, 2.9, 2.12, 2.15, 2.23, 2.24, 2.33 (from the textbook)
<nowiki>*</nowiki> Convex functions: 3.2, 3.15, 3.16, 3.36, 3.42
<nowiki>*</nowiki> Convex problems; 4.1, 4.65
<nowiki>*</nowiki> Duality: 5.1, 5.13, 5.38, 5.42