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| Fast linear algebra for multiarc trajectory optimization | 1 | |
| Lagrange multipliers with optimal sensitivity properties in constrained optimization | 15 | |
| An O(n[superscript 2]) algorithm for isotonic regression | 25 | |
| Knitro : an integrated package for nonlinear optimization | 35 | |
| On implicit-factorization constraint preconditioners | 61 | |
| Optimal algorithms for large sparse quadratic programming problems with uniformly bounded spectrum | 83 | |
| Numerical methods for separating two polyhedra | 95 | |
| Exact penalty functions for generalized Nash problems | 115 | |
| Parametric sensitivity analysis for optimal boundary control of a 3D reaction-diffusion system | 127 | |
| Projected Hessians for preconditioning in one-step one-shot design optimization | 151 | |
| Conditions and parametric representations of approximate minimal elements of a set through scalarization | 173 | |
| Efficient methods for large-scale unconstrained optimization | 185 | |
| A variational approach for minimum cost flow problems | 211 | |
| Multi-objective optimisation of expensive objective functions with variable fidelity models | 223 | |
| Towards the numerical solution of a large scale PDAE constrained optimization problem arising in molten carbonate fuel cell modeling | 243 | |
| The NEWUOA software for unconstrained optimization without derivatives | 255 |
| Price | Condition | Delivery | Seller | Action |
| $99.99 | Digital |
| WonderClub |
Patrick Schadle
reviewed Large-Scale Nonlinear Optimization on February 12, 2013
Large-Scale Nonlinear Optimization
I normally don't do this, but I'm going to copy/paste this review across three separate books: Chaitin's "The Unknowable", "The Limits of Mathematics", and "Exploring Randomness".
All three are all thin, overpriced, but very approachable books on Algorithmic Information Theory. Themes include:
- Undecidability, as the basis of formulating a new kind of randomness measure for numbers that have already been generated (not just restricting randomness measures to the processes that generate numbers).
- Chaitin's Omega constant, which is the probability that universal Turning machine will halt on random input. This constant is "maximally unknowable", but that doesn't stop one from performing math with it.
- Philosophy surrounding these topics.
One might underestimate Kolmogorov's contributions, given how ridiculously self-promoting Chaitin has been with this AIT. However, the topics are interesting enough that he's easy to forgive. And he uses many code demonstrations (LISP) to make concrete examples out of the math.
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