Introduction This unit focuses on the images of Glasgow and was first presented as a TV programme in 1993. It is not about Glasgow as such; it is about Glasgow's image. Images are representations of places: they are constructed and contested; images also represent multiple identities, uniqueness of place, interdependencies. There are many different ways of interpreting and representing the character and identity of a place – many different geographical imaginations. Identities of places ar
Visual Images in Social Sciences
How do social scientists use visual images?
What does a picture or image tell you? This unit is an introduction to analysing and interpreting photographs as social data. Who controls what the image is saying? You will look at how photographs provide visual evidence and how they can illustrate and support our ideas about society. This material is from our archive and is an adapted extract from Introducing the social sciences (DD100) which is no longer taught by The
2.3 The learner's repertoire
How do we learn? Understanding ‘how’ is the key to learning more effectively. This unit looks at the three main categories of theories: the acquisitive, constructivist and experiential models of learning. There is no right way to learn but developing an active approach will ensure that you are open to new ideas.
13. Convex Optimization II Lecture 13
Math, Technology, Algebra, calculus, geometry, electrical engineering, convex optimization, subgradient calculus, derivatives, basic inequality, function, algorithms, trust region, nonlinear optimal control, discretization, SCP, torque residuals, convex-c
12. Convex Optimization II Lecture 12
Math, Technology, Algebra, calculus, geometry, electrical engineering, convex optimization, subgradient calculus, derivatives, basic inequality, function, algorithms, sequential convex programming, alternating convex optimization, convex-concave, nonnegat
10. Convex Optimization II Lecture 10
Math, Technology, Algebra, calculus, geometry, electrical engineering, convex optimization, subgradient calculus, derivatives, basic inequality, function, algorithms, rate control, single commodity network flow, convex problem, dual decomposition, lagrang
9. Convex Optimization II Lecture 9
Math, Technology, Algebra, calculus, geometry, electrical engineering, convex optimization, subgradient calculus, derivatives, basic inequality, function, algorithms, separable problems, complicating variables, primal decomposition, dual, complicating con
8. Convex Optimization II Lecture 8
Math, Technology, Algebra, calculus, geometry, electrical engineering, convex optimization, subgradient calculus, derivatives, basic inequality, function, algorithms, ellipsoid method, convergence proof, inequality constraints, feasibility problems, deep
7. Convex Optimization II Lecture 7
Math, Technology, Algebra, calculus, geometry, electrical engineering, convex optimization, subgradient calculus, derivatives, basic inequality, function, algorithms, subgradient method,cutting plane, cutting plane method, analytic center, pruning constra
6. Convex Optimization II Lecture 6
Math, Technology, Algebra, calculus, geometry, electrical engineering, convex optimization, subgradient calculus, derivatives, basic inequality, function, algorithms, subgradient method,cutting plane, localization algorithms, lower bounds, stopping criter
5. Convex Optimization II Lecture 5
Math, Technology, Algebra, calculus, geometry, electrical engineering, convex optimization, subgradient calculus, derivatives, basic inequality, function, algorithms, subgradient method, stochastic programing, convergence proof, convex functions, adaptive
4. Convex Optimization II Lecture 4
Math, Technology, Algebra, calculus, geometry, electrical engineering, convex optimization, subgradient calculus, derivatives, basic inequality, function, algorithms, subgradient method, dual, constrained optimization, linear equality constraints, negativ
2. Convex Optimization II Lecture 2
Math, Technology, Algebra, calculus, geometry, electrical engineering, convex optimization, subgradient calculus, derivatives, basic inequality, function, algorithms, stepsize rules, convergence results, proofs, optimal step size, alternating projections,
1. Convex Optimization II Lecture 1
Math, Technology, Algebra, calculus, geometry, electrical engineering, convex optimization, subgradient calculus, derivatives, basic inequality, function, algorithms, convex analysis, nondifferentiable, subdifferential, weak subgradient calculus, strong s
18. Convex Optimization I Lecture 18
science, electrical, engineering, technology, convex, optimization, logarithmic barrier, reformulation, indicator, function, KKT, Lagrangian, Lipschitz, condition, geometric program, phase I
10. Convex Optimization I Lecture 10
science, electrical, engineering, technology, convex, optimization, estimation, approximation, norm, least-squares, Chebyshev, Huber, penalty, function, Tikhonov, regularization, linear, dynamical, system
9. Convex Optimization I Lecture 9
science, electrical, engineering, technology, convex, optimization, complementary, slackness, Karush, Kuhn, Tucker, conditions, KKT, Lagrangian, Slater's, condition, perturbed, sensitivity
3. Convex Optimization I Lecture 3
science, electrical, engineering, technology, convex, optimization, affine, eigenvalue, extended-value, extension, quadratic, function, epigraph, Jensen, inequality
4. Convex Optimization I Lecture 4
science, electrical, engineering, technology, convex, optimization, scalar, vector, Schur, complement, logarithm, quadratic, IRR, Jensen, inequality, Gaussian, integration, property, yield function
1. Convex Optimization I Lecture 1
science, electrical, engineering, technology, convex, optimization, least, squares, constraint, function, portfolio, circuit, data, fitting, ellipsoid, control, signal, processing
