Create sparse-constraint-systems

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book/src/how/sparse-constraint-systems

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+The design of Jolt (the built system) leans heavily on a simple but powerful technique that is not described in the Jolt paper.
+
+Jolt uses the technique to achieve what is sometimes called "a la carte" prover costs: for
+each cycle of the VM, the Jolt prover only "pays" for the primitive instruction
+that was actually executed at that cycle, regardless of how many instructions are
+in the Instruction Set Architecture (in Jolt's case, RISC-V). 
+
+This design allows Jolt to be extended to richer instruction sets
+(which is [equivalent to supporting different pre-compiles](https://a16zcrypto.com/posts/article/understanding-jolt-clarifications-and-reflections/#section--4)) 
+without increases in per-cycle prover cost.
+
+Here is the rough idea. Suppose a precompile is implemented via some constraint system (say, R1CS for concreteness). 
+As a first attempt at supporting the pre-compile, we can include into Jolt a single, data-parallel constraint system that "assumes" that the pre-compile is executed
+at every single cycle that the VM ran for. There are two problems with this. First,
+the pre-compile will not actually be executed at every cycle, so we need a way to "turn off"
+all the constraints for every cycle at which the pre-compile is not executed. Second, we do not want the prover to "pay" in runtime for 
+the constraints that are turned off.
+
+To address both of these issues, for each cycle $j$ we have the prover commit to a binary flag $b_j$ indicating
+whether or not that precompile was actually executed at that cycle. 
+
+For each cycle $j$ consider the $i$'th constraint that is part of the pre-compile, say: 
+$$ \langle a_i, z \rangle \cdot \langle b_i, z \rangle  - \langle c_i, z \rangle = 0.$$
+
+Here, one should think of $z$ as an execution trace for the VM, i.e., a list of everything
+that happened at each and every cycle of VM. In Jolt, there are under 100 entries of $z$ per cycle
+(though as we add precompiles this number might grow).
+
+We modify each constraint by multiplying the left hand side by the binary flag $b_j$, i.e., 
+$$b_j \cdot \left(\langle a_i, z \rangle \cdot \langle b_i, z \rangle  - \langle c_i, z \rangle \right) = 0.$$
+
+Now, for any cycle where this pre-compile is not executed, the prover can simply assign any variables 
+in $z$ that are only "used" by the pre-compile to 0. This makes these values totally "free" to commit to
+when using any commitment scheme based on elliptic curves (after we switch the commitment scheme to Binius,
+committing to 0s will no longer be "free", but we still expect the commitment costs to be so low that it won't matter a huge amount).
+These variables might not satisfy the original constraint but will satisfy the modified one
+simply because $b_j$ will be set to 0. We say that $b_j$ "turned off'' the constraint. 
+
+Moreover, in sum-check-based SNARKs for R1CS like Spartan, there are prover algorithms dating to CTY11 and CMT12 
+where the prover's runtime grows only with the number of constraints where the associated flag $b_j$ is not zero. 
+So the prover effectively pays nothing at all (no commitment costs, no field work) for constraints
+at any cycle where the pre-compile is not actually executed. We call these algorithms "streaming/sparse sum-check proving."
+In a happy coincidence, these are the *same* algorithms we expect to use to achieve a streaming prover (i.e., to control prover
+space *without* recursion). 
+
+More precisely, the prover time in these algorithms is about $m$ field operations *per round of sum-check*, where $m$ is the number of constraints with associated binary flag
+$b_j$ not equal to $0$. But if $n$ is the *total* number of constraints (including those that are turned off), 
+we can "switch over" to the standard "dense" linear-time sum-check proving algorithm after enough rounds $i$ pass 
+so that $n/2^i \approx m$. In Jolt, we expect this "switchover" to happen by round $4$ or $5$. 
+In the end, the amount of extra field work done by the prover owing to the sparsity will only be a factor of $2$ or so.
+
+Jolt uses this approach within Lasso as well. Across all of the primtive RISC-V instructions,
+there are about 80 "subtables" that get used. Any particular primitive instruction only needs
+to access between 4 and 10 of these subtables. We "pretend" that every primitive instruction
+actually accesses all 80 of the subtables, but use binary flags to "turn off" any subtable
+lookups that do not actually occur (i.e., that is, we tell the grand product 
+in the guts of the Lasso lookup argument for that subtable to ``ignore'' that lookup). 
+This is why about 93% of the factors multiplied in Jolt's various grand product arguments are 1. 
+The same algorithmic approach ensures that the "turned off lookups" do not increase
+commitment time, and that our grand product prover does not pay any field work for the "turned off" subtable lookups. 
+
+There are alternative approaches we could take to achieve "a la carte" prover costs, e.g., [vRAM](https://web.eecs.umich.edu/~genkin/papers/vram.pdf)'s approach
+of having the prover sort all cycles by which primitive operation or pre-compile was executed at that cycle
+(see also the much more recent work [Ceno](https://eprint.iacr.org/2024/387)).
+But the above approach is compatable with a streaming prover, avoids committing to the same data multiple times,
+and has other benefits.
+
+We call this technique (fast proving for) "sparse constraint systems". Note that the term sparse here
+does not refer to there being the sparsity of the R1CS constraint matrices themselves, 
+but rather to almost all of the left hand sides of the constraints being $0$
+when the constraints are evaluated on the valid witness vector $z$.
+