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README.md

@@ -171,5 +171,7 @@ The *nice* case of diagonalization is when you have **orthonormal eigenvectors**
   - Dense linear algebra: Gaussian elimination, QR, eigenvalues, SVD, etcetera, assuming that A is just m×m numbers with no special structure.  Cost is Θ(m³), memory is Θ(m²).  Usually you run out of memory before running out of time! (m ∼ 10⁴ is close to filling up memory, but runs in only few minutes.)
   - Sparse-direct methods: For [sparse matrices](https://en.wikipedia.org/wiki/Sparse_matrix) (mostly 0), only store and compute with nonzero entries.  If a clever ordering is chosen for rows/cols, Gaussian elimination can often produce mostly sparse L and U factors!  (This is what `A \ b` does in Julia if `A` is a [sparse-matrix type](https://github.com/JuliaSparse/SparseArrays.jl).)   But for very big problems even these methods can eventually run out of memory.
   - Iterative methods: start with a "guess" for x (usually x=0 or x=random), and iteratively make it closer to a solution **using only A-times-vector operations** (and linear combinations and dot products).  Requires a fast A-times-vector, e.g. if A is sparse, low rank, a convolution, or some combination thereof.   Modern methods include [GMRES](https://en.wikipedia.org/wiki/Generalized_minimal_residual_method), [BiCGSTAB(ℓ)](https://en.wikipedia.org/wiki/Biconjugate_gradient_stabilized_method), [conjugate gradient (CG)](https://en.wikipedia.org/wiki/Conjugate_gradient_method), and others.
+* pset 2 solutions: coming soon
+* pset 3 (due 3/17): coming soon
 
 **Further reading:** For Gram–Schmidt and QR, see further reading for lecture 9.  Lecture II.1, [OCW video lecture 10](https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/resources/lecture-10-survey-of-difficulties-with-ax-b/). Sparse-direct solvers are described in detail by the book *Direct Methods for Sparse Linear Systems* by Davis.  Iterative methods: More advanced treatments include the book *Numerical Linear Algebra* by Trefethen and Bao, and surveys of algorithms can be found in the *Templates* books for [Ax=b](http://www.netlib.org/linalg/html_templates/Templates.html) and [Ax=λx](http://web.cs.ucdavis.edu/~bai/ET/contents.html).  [Some crude rules of thumb](https://github.com/mitmath/18335/blob/spring20/notes/solver-options.pdf) for solving linear systems (from 18.335 spring 2020).