Advanced techniques for determining complexity lower bounds Some of you may have been following this question, which was closed due to not being research level. So, I'm extracting the part of the question which is at a research level.

Beyond the "simpler" techniques, such as reducing to sorting or an EXPTIME-complete problem, what techniques have been used to prove lower bounds for the time complexity of a problem? 

In particular:


What are the "cutting-edge" techniques that have been developed in the last decade?
Can techniques from Abstract Algebra, Category Theory, or other branches of typically "pure" mathematics be applied? (For example, I often hear mention of the "algebraic structure" of sorting, without any real explanation of what this means.)
What are significant but lesser-known results for lower-bound complexity?
Lower bounds for algebraic circuits
In the setting of algebraic circuits, where a lower bound on circuit size is analogous to a lower bound on time, many results are known, but there are only a few core techniques in the more modern results. I know you asked for time lower bounds, but I think in many cases the hope is that the algebraic lower bounds will one day lead to Boolean/Turing machine lower bounds. These results often use deeper techniques from &quot;pure math&quot; as you put it.
I. The degree bound.
Strassen showed that the log of the degree of a certain algebraic variety associated to a (set of) function(s) is a lower bound on the algebraic circuit size of computing those functions.
II. Connected components (or more generally the dimension of any higher homology group).
Ben-Or showed that the size of a real algebraic decision tree deciding membership in a (semi-algebraic) set is at least $\log C$ where $C$ is the number of connected components of that set. Ben-Or used this to prove an $\Omega(n \log n)$ lower bound on sorting (well, element distinctness, but element distinctness reduces to sorting) in the real algebraic decision tree model. Yao extended this from connected components to the sum of the Betti numbers and proved optimal lower bounds for other problems (like $k$-equals)). In a different paper Yao extended this to algebraic decision trees over the integers.
III. Partial derivatives.
This has been the workhorse of many of the modern algebraic circuit lower bounds. I believe partial derivatives were first used to prove a lower bound by Baur-Strassen, where they showed that computing all the first partials of $f$ can be done in size $5s$ where $s$ is the size needed to compute $f$. Combined with Strassen's degree bound, this gave $\Omega(n \log n)$ size lower bounds on various functions, which are still the strongest lower bounds on the size of unrestricted arithmetic circuits for an explicit function.
The more recent use of partial derivatives seems to stem from a paper of Nisan's in which he proved lower bounds on noncommutative circuits by considering the dimension of the space of all partial derivatives. This was used to prove lower bounds on restricted kinds of depth-3 circuits by Nisan-Wigderson, and similar ideas were used to prove lower bounds on multilinear formula size by Raz (and related models by Raz and collaborators). The very recent depth 4 and depth 3 lower bounds by Gupta, Kayal, Kamath, and Saptharishi use a generalization of this idea, to count the dimension of the space of &quot;shifted partial derivatives&quot; - where you can take partial derivatives and then multiply by any monomials of a given degree.  Improving the GKKS result to a super-polynomial size lower bound on permanent (which would imply $\mathrm{VP} \neq \mathrm{VNP}$) may &quot;just&quot; be a matter of getting a better understanding of the ideal generated by permanental minors (see the conjecture at the end of their paper).
IV. Defining equations for varieties.
The idea here is to associate to &quot;easy functions&quot; a certain algebraic variety, find equations that vanish on this variety, and show that these equations do not vanish on your &quot;hard function.&quot; (Hence proving that your hard function is not in the variety of easy functions, so that it's actually hard.) Especially useful in lower bounds on matrix multiplication. See Landsberg--Ottaviani on the arXiv for the latest, and references to prior lower bounds.
(In fact, I, II, and III above can all be viewed as special cases of finding defining equations for certain varieties, though the proofs that use I,II,III are essentially never phrased that way, as there wasn't really a need to.)
V. Representation theory, esp. as in geometric complexity theory.
Actually, also used by Landsberg--Ottaviani to find some equations for a certain variety. Also used by Burgisser-Ikenmeyer to get a &quot;purely&quot; representation-theoretic proof of a slightly weaker lower bound on matrix multiplication. Conjectured by Mulmuley and Sohoni (cf. &quot;Geometric Complexity Theory I &amp; II&quot;) to be useful to resolve $\mathrm{VP}$ vs $\mathrm{VNP}$ and ultimately $\mathrm{NP}$ vs. $\mathrm{P/poly}$.