When Colombus left Spain in 1492, sailing West, he knew that the Earth was round and was expecting to land in Japan. Seventeen centuries earlier, around 200 BC, Eratosthenes had shown that its circumference was 40,000 km, just by a smart use of mathematics, without leaving his home town of Alexandria. Since then, we have learned much more about Earth: it is a planet, it has an inner structure, it carries life , and at every step mathematics have been a crucial tool of discovery and understanding. Nowadays, concerns about the human footprint and climate change force us to bring all this knowledge to bear on the global problems facing us. This is the last challenge for mathematics: can we control change?
This is a two-part lecture, investigating how our idea of the world has influenced the development of mathematics. In the first lecture on July 15, I will describe the situation up to the twentieth century, in the second one on July 17 I will follow up to the present time and the global challenges humanity and the planet are facing today.
When Colombus left Spain in 1492, sailing West, he knew that the Earth was round and was expecting to land in Japan. Seventeen centuries earlier, around 200 BC, Eratosthenes had shown that its circumference was 40,000 km, just by a smart use of mathematics, without leaving his home town of Alexandria. Since then, we have learned much more about Earth: it is a planet, it has an inner structure, it carries life , and at every step mathematics have been a crucial tool of discovery and understanding. Nowadays, concerns about the human footprint and climate change force us to bring all this knowledge to bear on the global problems facing us. This is the last challenge for mathematics: can we control change?
This is a two-part lecture, investigating how our idea of the world has influenced the development of mathematics. In the first lecture (July 15), I will describe the situation up to the twentieth century, in the second one (July 17) I will follow up to the present time and the global challenges humanity and the planet are facing today.
A general matrix A can be reduced to tridiagonal form by orthogonal
transformations on the left and right: UTAV = T. We can arrange that the
rst columns of U and V are proportional to given vectors b and c. An iterative
form of this process was given by Saunders, Simon, and Yip (SINUM 1988) and
used to solve square systems Ax = b and ATy = c simultaneously. (One of the
resulting solvers becomes MINRES when A is symmetric and b = c.)
The approach was rediscovered by Reichel and Ye (NLAA 2008) with emphasis
on rectangular A and least-squares problems Ax ~ b. The resulting solver was
regarded as a generalization of LSQR (although it doesn't become LSQR in
any special case). Careful choice of c was shown to improve convergence.
In his last year of life, Gene Golub became interested in \GLSQR" for
estimating cTx = bTy without computing x or y. Golub, Stoll, and Wathen
(ETNA 2008) revealed that the orthogonal tridiagonalization is equivalent to a
certain block Lanczos process. This reminds us of Golub, Luk, and Overton
(TOMS 1981): a block Lanczos approach to computing singular vectors.
On solving indefinite least squares problems via anti-triangular factorizations:
Nicola Mastronardi, IAC-CNR, Bari, Italy and Paul Van Dooren, UCL, Louvain-la-Neuve, Belgium
Suppose you have a collection of data matrices each of which has the same number of columns. The HO-GSVD can be used to identify common features that are implicit across the collection. It works by identifying a certain (approximate) invariant subspace of a matrix that is a challenging combination of the collection matrices. In describing the computational process I will talk about the Higher Order
CS decomposition and a really weird optimization problem that I bet you have never seen before! Joint work with Orly Alter, Priya Ponnapalli, and Mike Saunders.