1. Algorithms vs. Circuit Lower Bounds
Igor Carboni Oliveira
Columbia University
CCI Meeting – Princeton, February 2014

2. Algorithm for hard computational problem
What this talk is about
“Explicit” function f (P, EXP, NP, …)
not in nonuniform circuit class
(ACC, TC0, NC1, …).
Algorithm for hard computational problem
Sketch some proof techniques used in different contexts + point out some observations from [O’13] + mention some directions.

3. This talk:
Compression
Important:
No assumptions on algorithm.
Learning
Satisfiability
Non-uniform
Circuit Lower Bounds
Discuss techniques for deterministic, nondeterministic, randomized algorithms.
Why should you care?
only known approach to some CLBs.
Nontrivial proofs for tautologies
Derandomization
Useful Properties (CLBs around NEXP)

4. P ⊆ NP ⊆ PH ⊆ PSPACE ⊆ EXP ⊆ NEXP, BPEXP ⊆ MAEXP
Recall that:
SAT algorithm [Williams’11]
Learning algorithm [LMN’89]
NEXP not here! [Williams’11]
Contains NEXP?
MAEXP not here! [BFT’98]
a few nontrivial (restricted) SAT algorithms [IPS13], [Williams’14], …
AC0 ⊆ ACC ⊆ TC0 ⊆ NC1 ⊆ AC1 ⊆ NC2 … ⊆ P/poly
P ⊆ NP ⊆ PH ⊆ PSPACE ⊆ EXP ⊆ NEXP, BPEXP ⊆ MAEXP
Open. NEXP, BPEXP ⊆ TC0 (depth two)?

5. A warm-up example

6. 80’s: super fast SAT implies strong CLBs
[Karp-Lipton-Meyer’80] + [Kannan’82]
If P = NP then EXP requires circuits of exponential size.
Proof sketch.
1) P = NP implies P = PH (easy).
2) P = PH implies EXP = EXPH (padding).
3) EXPH contains problems of exponential circuit complexity [Kannan’82].
Strong lower bound from a very strong assumption.

7. Three decades later…

8. Nontrivial proofs for C-tautologies implies CLBs
Roadmap
(*approximate*)
Circuit lower bound obtained from new algorithm.
BFNW’93, NW’94, IW’97, …
Hardness vs.
Randomness
Kabanets’01:
Easy witness method
Know even more about ACC!
EXP ⊈ ACC or NTIME[nlogc n] ⊈ ACC.
Williams’11:
C-SAT Algorithms,
NEXP ⊈ ACC.
IKW’02:
NEXP vs. P/poly
Williams’10:
“nontrivial” P/poly-SAT algorithms and lower bounds
Tourlakis’01, FLvMV’05:
Tight Cook-Levin reduction
Yao’90, BT’94, AG’94: Structural results for ACC.

9. A closer look: Nontrivial proofs imply CLBs
Proposition ([Williams’11], Informal). Let C be a circuit class. If there exists a proof system P such that every polynomial size tautology over n variables from C admits a proof that can be checked in time 2n/s(n), then NEXP ⊈ C.
Deterministic SAT algorithms: particular case. *Find* proofs in time 2n/s(n).
Formally:
What is a circuit class? C = TC0 depth two?
C ≠ C (loss in the reduction).

10. Nontrivial proofs imply CLBs
[Williams’10] C = C = P/poly. [Williams’11] C contains AC0, closed under composition. Proofs for tautologies in depth 2d + O(1) imply CLBs for depth d. ACC lower bound.
Next step: lower bounds against classes such as depth-two TC0?
[O’13] Relax assumptions on C + tighter connection + simpler proof:
“Proofs for depth d + 2 yield circuit lower bounds against depth d”.
NEXP ⊈ ACC o TH [Willliams’14].
Proof explores trick introduced in [O’13].

11. = f (x) AND g(x) = h(x), for every x in {0,1}n. Tautology?
Def. A circuit class C is reasonable if:
The constant function 0 is in C;
C is (effectively) closed under complementation;
Gates may have direct access to constant inputs 0/1;
C ⊆ P/poly.
Examples: depth-d TC0, AC0, NC1, P/poly.
Computational Problem: Equiv-AND-C
Def. Given circuits f, g, h from C, check if:
f (x) AND g(x) = h(x), for every x in {0,1}n.
AND f g h =
Tautology?
Def. Nontrivial proofs: can be checked by a uniform algorithm in time 2n/s(n).
Proposition [O’13]. If C is reasonable and polynomial size “EQUIV-AND-C tautologies” admit nontrivial proofs, then NEXP ⊈ C.

12. Proposition [O’13]. If C is reasonable and polynomial size EQUIV-AND-C tautologies admit nontrivial proofs, then NEXP ⊈ C.
Proof sketch (following [Williams’10]).
NTIME[2n] ⊈ NTIME[2n/n100]
Assume: (1) NEXP ⊆ C
(2) ∃ nontrivial proofs.
We contradict the Nondeterministic Time Hierarchy Theorem.
Lemma 1 (Tourlakis’01, FLvMV’05: “tight Cook-Levin reduction”). Every language L ∈ NTIME[2n] can be reduced to (succinct) 3SAT instances of size poly(n)2n.
There is a polynomial time algorithm that, given x (instance of L), outputs a circuit Cx from P/poly over n + O(log n) inputs that:
1) Given an index i ∈ {0,1}n + O(log n) , Cx prints the i-th clause of formula Fx.
2) Fx is satisfiable x ∈ L.
Fx : exponentially many clauses and variables. Succinct representation given by circuit Cx.

13. Recall: NEXP ⊆ C ⊆ P/poly
(C is “reasonable”)
Proposition [O’13]. If C is reasonable and polynomial size EQUIV-AND-C tautologies admit nontrivial proofs, then NEXP ⊈ C.
Proof sketch.
Lemma 2 (IKW’02, “hardness vs. randomness, easy witness, diagonalization”). If NEXP ⊆ P/poly then every NEXP-verifier V admits succinct witnesses: If V(x,w) = 1 for some w (of exponential length), then there is some w* such that:
V(x,w*) = 1;
w* is the truth table of some polynomial size circuit D.
Lemma 2 implies that:
Fx is satisfiable Fx(tt(D)) = 1, for some circuit D from P/poly over n + O(log n) variables.

14. How to check whether Fx is satisfiable?
Build a new circuit.
Proposition [O’13]. If C is reasonable and polynomial size EQUIV-AND-C tautologies admit nontrivial proofs, then NEXP ⊈ C.
Recall: Trying to decide L ∈ NTIME[2^n] in less (nondeterministic) time.
Given instance x, poly size circuit Cx prints i-th clause of 3-CNF Fx.
(Nondeterministic) Algorithm for L: Guess circuit D that outputs assignment for Fx.
Combines circuits Cx and D into a new circuit Ex over n + O(log n) variables such that: Ex : Given input i (index of a clause), use Cx to print this clause, and three copies of D to obtain values for variables in this clause.
Ex outputs 1 on input i ∈ {0,1}n + O(log n) i-th clause is satisfied by “D”
Therefore:
x in L iff Fx satisfiable iff  P/poly circuit D with Fx(tt(D)) = 1 iff Ex is a tautology

15. How to obtain an equivalent circuit from circuit class C?
Proposition [O’13]. If C is reasonable and polynomial size EQUIV-AND-C tautologies admit nontrivial proofs, then NEXP ⊈ C.
Proof sketch.
x in L Ex is a tautology
So far: poly time computation + guessing D (poly size string) = NP computation.
Problem: How to prove that Ex is a tautology and put L in NTIME[<<2n]? Ex is a “P/poly-circuit”.
[Williams’11]
[SW’12]
[JMV’13]
[SV’14]
[Williams’10]
How to obtain an equivalent circuit from circuit class C?
We describe next the method from [O’13].

16. Proposition [O’13]. If C is reasonable and polynomial size EQUIV-AND-C tautologies admit nontrivial proofs, then NEXP ⊈ C.
Proof sketch.
Fact. By assumption P ⊆ NEXP ⊆ C. Therefore any function f: {0,1}n to {0,1} computed by a “P/poly” circuit A of size na admits a circuit from C of size nb.
Why? “Circuit evaluation” problem is in P (instances: <circuit, input>), and P ⊆ C.
Hardwire A’s description in new circuit from C computing “Circuit evaluation”.
AND
f in C
g in C
h in C = ?
Equiv-AND-C
IMPORTANT: function computed at this AND gate admits C-circuits of size nb.
AND
NOT
Circuit Ex:
We can assume Ex uses AND (fan-in two), NOT gates only.
Every subcircuit of Ex has size na. Guess equivalent C-circuits of size nb.
Use nontrivial EQUIV-AND_C proofs to check that these circuits are equivalent.
Obtain final C-circuit H equivalent to Ex. Finally, guess a proof that H is a tautology.

17. An example from derandomization: PIT

18. Derandomization implies CLBs
[KI’04] PIT = language of all arithmetic circuits computing zero polynomial over Z.
PERM = problem of computing the permanent over integer matrices. Proposition. If PIT ∈ NSUBEXP, then at least one of the following results hold:
(1) NEXP ⊈ P/poly; or
(2) PERM ∉ AlgP/poly.
Follows from downward reducibility of Permanent!
Let’s derive it from William’s theorem.
Lemma [KI’04], [AvM’11]. There exists an efficient algorithm such that:
Input: Arithmetic Circuit An. Output: Arithmetic Circuit Cm Guarantee: An computes PERM of n x n matrices iff Cm ∈ PIT.

19. Connection may even improve [KI’04]
Proposition [KI’04] . If PIT ∈ NSUBEXP, then at least one of the following results hold:
(1) NEXP ⊈ P/poly; or
(2) PERM ∉ AlgP/poly.
EXP ⊈ P/poly
Proof by contradiction. Assume PIT ∈ NSUBEXP, NEXP ⊆ P/poly, PERM ∈ AlgP/poly.
[Williams’10] (contrapositive): If NEXP ⊆ P/poly then P/poly tautologies admit only trivial proofs.
MetaMetaTheorem [O’13]: Proof shows that these meta results are in fact connected: Improvements in Williams’ framework propagate to [KI’04].
EXP ⊆ P/poly
Subexponential size proofs for P/poly tautologies: Given poly size circuit C, is C a tautology?
Problem in PH By Toda’s Thm, reduces to P#P By Valiant’s Thm, reduces to PERM Since PERM ∈ AlgP/poly, can guess a small arithmetic circuit A for PERM Using previous Lemma, can check if A is correct by solving PIT (in NSUBEXP) Answer initial query (correctly!).
Connection may even improve [KI’04]
(work in progress)

20. Π distinguishes a hard function f from all functions in C.
Easier way to prove meta theorems of the form
“algorithm implies circuit lower bounds”?
Useful Properties [Williams’13]: “Characterizing CLBs around NEXP”. Def. Property of Boolean functions = subset of all Boolean functions. A property Π is useful against circuit class C[poly] if: ∀ k ∃ infinitely many n’s such that:
1) Π(fn) = 1 for at least one function fn: {0,1}n to {0,1} 2) Π(gn) = 0 for all gn : {0,1}n to {0,1} computed by circuits from C of size nk. We say that Π is a Γ-Property if it can be decided in complexity class Γ (on inputs of size N = 2n). C Π f
Π distinguishes a hard function f from all functions in C.

21. A P-property is an algorithm!
Useful Properties versus Circuit Lower Bounds
A P-property is an algorithm!
[Williams’13] “There exists a P-property useful against C iff NEXP ⊈ C”.
If we insist that useful properties are defined only over truth tables, i.e., inputs of size N = 2n (following previous definition), then:
What matters for this talk: Algorithm running in time polynomial in N (truth table size) that distinguishes hard function from functions in C[poly]:
NEXP ⊈ C[poly].
Proposition 1. There exists a P/log N-property useful against C[poly] iff NEXP ⊈ C[poly].
What if we insist on properties computed without advice?
[O’13] Proposition 2. a) If for every constant d there exists a P-property useful against C[nlogd n], then NE∩i.o.coNE ⊈ C[nlog n]. b) If NE∩coNE ⊈ C[poly] then there is a P-property useful against C[poly].
Check [O’13] for more details.

22. Some direct consequences (I)
Deterministic!
[FK’06] “Learning yields circuit lower bounds” Proposition. Let C be any circuit class. If there exists a subexponential time algorithm that exact learns any concept from C using MQ and EQ queries, then EXPNP ⊈ C.
Equivalence Query oracle EQf:
Given (the representation) of a function g:{0,1}n -> {0,1}, outputs “yes” if g ≡ f, or an input w such that g(w) ≠ f(w) otherwise.
Membership Query oracle MQf:
Given any x ∈ {0,1}n, returns f(x).
Original Proof: Karp-Lipton Collapse, Properties of PERM,
Relativized Time Hierarchy, + other ideas…
All functions in C: learned in time << 2n. Random function: cannot be learned in time << 2n. Given truth table of size N = 2n, try to learn it. Efficient algorithm in N (can answer MQ and EQ).
P-property useful against C. NEXP ⊈ C.

23. Some direct consequences (II)
[KK’13, CKKSZ’13] “Approximate Compression yields circuit lower bounds” Problem: C[poly] circuit class. Given the truth-table of a function f in C (of size N= 2n), output in time poly(N) a circuit of size << 2n /n that 0.51-approximates f. Proposition. If C admits efficient compression algorithms, then NEXP ⊈ C[poly].
Using the same argument, follows immediately from “Useful Properties”: random functions cannot be compressed (not even approximately).
This approach
is not always optimal! Exact learning leads to stronger lower bounds: elementary proof in [KKO’13].
Using [O’13], compression of quasi-poly size circuits from C yields even stronger CLBs!

24. CLBs from randomized algorithms

25. CLBs from randomized (learning) algorithms
Proposition [FK’06]. Let C ⊆ P/poly be any circuit class. If C is PAC-learnable with membership queries under the uniform distribution in polynomial time, then BPEXP ⊈ C[poly].
Proposition [KKO’13]. If C is PAC learnable with membership queries under
the uniform distribution in polynomial time, then either:
(1) PSPACE ⊈ C[poly]; or
(2) PSPACE ⊆ BPP.
Can be combined with [Santhanam’07] to get lower bounds for BPP/1 [Volkovich’14]
Removing (2) from statement implies PSPACE ≠ BPP (unconditionally)

26. Let f be a PSPACE-complete function in C[poly].
Proposition [KKO’13]. If C is PAC learnable with membership queries under
the uniform distribution in polynomial time, then either:
PSPACE ⊈ C[poly]; or
PSPACE ⊆ BPP.
Assume: C is learnable + PSPACE ⊆ C[poly]. Need to prove that PSPACE ⊆ BPP. Plan:
Let f be a PSPACE-complete function in C[poly].
“Use efficient (randomized) learning algorithm to compute f in BPP”.

27. Can answer MQs if can compute g on smaller instances!
Problem 1: (PAC) Learner provides hypothesis that is correct on 99% of the inputs. BPP Algorithm: must be correct on every input (with high probability).
Can correct hypothesis from learner!
Idea: There is a PSPACE-complete problem f that is self-correctible [BF’90]: if hypothesis for f is correct on most inputs, then can compute f correctly on every input x with high probability.
Problem 2: Learning algorithm asks MQs: given x, what is f(x)? Exactly what we are trying to compute!
g = TQBF!
Idea: There is a PSPACE-complete problem g that is downward self-reducible: Can compute g(x) in polynomial-time if can compute g on smaller instances.
Can answer MQs if can compute g on smaller instances!
Theorem [TV’07]. There exists a language L* such that:
L* is PSPACE-complete.
L* is self-correctible.
L* is downward self-reducible.

28. Limitations of these approaches
Which algorithms can lead to new CLBs?
Narrow view… combine different techniques!
Learning. black-box. No PRFs in circuit class C.
Compression. Natural proofs barrier as well...
Derandomization of PIT. Extensions. Strong algorithmic assumption?
Satisfiability. Non black-box. Weak assumption. Partial progress on TC0.
What about NC1? P/poly? Stronger CLBs? Hard to design algorithms…

29. Designing nontrivial algorithms in the “CLB World”?
Algorithms used in previous CLB proofs can be implemented!
Designing nontrivial algorithms in the “CLB World”?
Complexity Theory Framework
Design of Algorithm
CLB connection
A very simple example from learning theory.
Membership Queries versus Random Examples.
DNFs learnable in poly time under the uniform distribution with MQs [Jackson’94].
Can we learn DNFs efficiently using random examples? Open.

30. Designing nontrivial algorithms in the CLB World?
In a circuit lower bound proof, everything that can be learned with MQs in poly time can be learned with random examples only.
Proof sketch.
CLB proof against C: can assume from the very beginning that PSPACE ⊆ C.
[KKO’13] C PAC-learnable with MQs in poly time then either:
PSPACE ⊈ C[poly] or PSPACE ⊆ BPP.
Therefore learning C with MQs implies PSPACE ⊆ BPP.
But then C can be learned with random examples only by finding a consistent hypothesis (“Occam’s Razor”).

31. Final Remarks
Use assumption? Can we design nontrivial algorithms using the extra power provided by our assumption (“no CLB”)?
Help from proof complexity? Easier than obtaining deterministic SAT algorithms?
Partial converse to Williams’ program? Is the existence of nontrivial proofs *necessary* for CLBs against C?
Suppose NP ⊈ C, and that this proof can be formalized in some bounded arithmetic theory.
Q. Is it the case that every C-tautology admits a nontrivial proof?

32. Thank you!