Lower Bounds on Black-Box Reductions of Hitting to Density Estimation Roei Tell, Weizmann Institute of Science STACS, March 2018

A textbook question How to catch a lion in the desert?

(Use binary search.) (It’s optimal [KUW88].)

Our main question How to catch a lion in a lions’ den? where lions are in abundance (Wait, if lions are almost everywhere, can’t we just look around and find one?)

The problem

reduction between derandomization problems

Density Estimation > canonical decision problem; equivalent to prP = prBPP

> Input:

A circuit C:{0,1}n → {0,1}

> Output:

δ ∈ [0,1] st Prx[C(x) = 1] = δ ± ε

⇒ for ε = ⅓, 1/log(n), 1/poly(n)

> reduces to the decision problems “is δt+ε”, for t = ε, 2⋅ε …, 1-ε

Density Estimation on Subcubes > canonical decision problem; equivalent to prP = prBPP

> Input:

A circuit C:{0,1}n → {0,1}, a subcube S ⊆ {0,1}n

> Output:

δ ∈ [0,1] st Prx∈S[C(x) = 1] = δ ± ε

⇒ for ε = ⅓, 1/log(n), 1/poly(n)

“Hitting” a circuit > canonical search problem

> Input:

A circuit C:{0,1}n → {0,1} that accepts most1 of its inputs

> Output:

x ∈ {0,1}n such that C(x) = 1

1

can also assume that C accepts ≥ ε of its inputs

The main problem > formal setting

1. Unknown set S(=C-1(1)) ⊆ {0,1}n is dense, |S| ≥ 2n/2 2. Can query any Q ⊆ {0,1}n for “|S ∩ Q|/|Q| ± ε” 3. Goal: Efficiently find s ∈ S > # queries > # iterations > lower bounds for any query Q, upper bounds only use subcube queries

Several motivations to study > derandomization, a “textbook” problem

1. Reduction between canonical problems in derandomization (a-la [Gol10]) 2. Extension of a classical problem (e.g., [KUW88]) 3. Relaxation of an open problem [MNN94] > the full open problem is still open!

The naive solution equipartition and recurse

The naive solution > equipartition and recurse, single bit

1. s0 ← λ 2. for i = 1 … n 2a. estimate density of S in subcubes si-10*, si-11* 2b. extend si-1 by bit with largest density

The naive solution > equipartition and recurse, single bit

> n queries ⇒ optimal (up to additive O(1))

> highly iterative > requires small error ε < 1/2n

The naive solution (trade-off) > equipartition and recurse, l bits

1. s0 ← λ 2. for i = 1 …n/l 2a. estimate density of S in subcubes si-1σ*, ∀σ ∈ {0,1}l 2b. extend si-1 by σ with largest density

The naive solution (trade-off) > equipartition and recurse, l bits

> n/l iterations > requires error ε ≤ l/n > 2l ⋅ (n/l) queries ⇒ parallelizing requires exp-many queries ⇒ large error forces exp-many queries

The main result

The main result > informal

The naive solution is optimal In some settings, it’s optimal even given some “non-black-box” information about S.

> parallelization and/or large error require exp-many queries

Rest of the talk 1. Details 2. High-level outline of proofs 3. Basic open questions ⇒ do we really “don’t know how to prove” these?

The problem resists parallelization

Parallel algorithms > general form of a parallel algorithm for the problem

1. In each iteration, 1a. issue p queries Q1 … Qp 1b. receive p answers δ1 … δp, each with error ε 2. Output some s ∈ S

> naive solution: p = 2l, number of iterations = n/log(p)

Lower bound on parallel algorithms > Thm 1: When in each iteration we can use p density estimations with error ε, the number of iterations required to solve the problem is at least n log(p+1) + log(1/ε)

> in particular, for ε ≥ 1/pO(1) the lower bound is ≈ n/log(p)

Lower bound on parallel algorithms > some “non-black-box” information does not help

> Thm 1(a): When each query is a subcube, the lower bound in Thm 1 holds even if S=C-1(1) where C is a CNF of size ≤ n2⋅p > Thm 1(b): When each query has circuit complexity m, the lower bound in Thm 1 holds even if S=C-1(1) for a circuit C of size ≤ n⋅p⋅m > size of C larger than #queries (this is necessary)

The problem is hard when the error is large

Algorithms with large error > general form of a parallel algorithm for the problem

> For fixed error ε, what is minimal #queries? > regardless of iterations

> Naive solution: #queries = 2O( ε⋅n ) > for ε ≤ log(n) / n this is polynomial > for ε = Ω(1)

this is trivial

Large error ⇒ many queries > Thm 2: When the estimation error is ε ≥ 4log(n)/n, the number of queries required to solve the problem is 2Ω( ε⋅n ) , which matches the naive upper-bound. For ε ≥ ¼+Ω(1), at least 2n/poly(n) queries are needed.

The proof approach

Adversary argument 1. Simulate any algorithm A for the problem 2. Answer queries consistently wrt some dense set S 3. Don’t allow A to “isolate” s ∈ S too quickly ⇒ algorithm looking for small “positive” sets (Q∩S≠ø) ⇒ “slow down” its progress (make sure that “positive” Q’s are not small)

Adversary argument > Key challenge: Engineer answers to queries that are consistent with some dense set S ⇒ not hard when queries are “Q∩S = ø?“ [KUW88] ⇒ challenging when queries are for “|Q∩S|/|Q|“ precisely ⇒ this work: tractable when queries are for “|Q∩S|/|Q| ± ε“!

Adversary argument: First approach > underlying the proof of Thm 1

1. Start with tentative set S={0,1}n 2. Allow “big” queries; answer honestly 3. Disallow “small” queries; erase from tentative set ⇒ def of “small” decays exponentially across iterations ⇒ fixing any queried-set Q, we won’t erase too much from “tentative set ∩ Q” in subsequent iterations ⇒ thus, “final set ∩ Q” resembles our “real-time” answer

Adversary argument: Second approach > warm-up: constructing a set S with density 1.99⋅ε

1. Answer each query of size ≥ n with estimate “ε“ ⇒ essentially meaningless

2. Answer each query of size < n with estimate zero ⇒ no “positive” singletons isolated

3. In the end, draw a random set of density ≈ 1.99⋅ ε ⇒ among inputs not participating in small queries

Adversary argument: Second approach > underlying the proof of Thm 2

1. Answer each query Q according to some fixed set of rules (answer only depends on |Q|) 2. In the end, draw a set from carefully-chosen distribution (which depends on queries) ⇒ no positive singletons isolated ⇒ random set has correct density in all queries, whp

An observation Both approaches fail when ε = 0! what happens when the algorithm gets the exact density of S in Q ( i.e., the value |Q ∩ S|/|Q| )?

> proof techniques crucially rely on the presence of an estimation error

Basic open problems “surely we can prove this”

A basic open problem > posed by Motwani, Naor, and Naor (1994)

1. Unknown set S ⊆ {0,1}n is dense, |S| ≥ 2n/2 2. Can query any Q ⊆ {0,1}n for |S ∩ Q|/|Q| (precisely!) 3. Goal: Efficiently find s ∈ S > # queries > # iterations > precise density answers - less relevant to derandomization

A partial answer in this work > lower bound on #queries

> Thm 3: When there is no estimation error (i.e., ε = 0) the number of queries required to solve the problem is n-O(1)

.

> actual bound tight up to a single bit; proof is not trivial

Part that remains open > lower bound on #iterations

> Conj 4: When there is no estimation error (i.e., ε = 0) the number of iterations required to solve the problem is Ω( n/log(p) ) , (where p is the number of queries in each iteration).

> recall that Thm 1 gives lower bound of n/( log(p) + log(1/ε) )

Another open problem > “constructive” version of Thm 2

> Recall Thm 2: When the estimation error is ε > 0, the number of required queries is 2Ω( ε⋅n ). > Question: Does this hold even if we are guaranteed that S=C-1(1) for a “simple”1 circuit C?

1 e.g., DNFs of size 2{Ω(n)}

Key takeaways 1. “Search to decision” reductions in derandomization > the naive solution is optimal in a “black-box” setting

2. Interesting, under-studied “textbook” problem 3. Basic questions still unanswered: > is the naive solution optimal when ε = 0?

Thank you! ⇒ “search-to-decision” in derandomization ⇒ interesting, under-studied “textbook” problem ⇒ is the naive solution actually optimal in all settings?

Lower Bounds on Black-Box Reductions of Hitting to ...

Start with tentative set S={0,1}n. 2. Allow “big” queries; answer honestly. 3. Disallow “small” queries; erase from tentative set. ⇒ def of “small” decays exponentially across iterations. ⇒ fixing any queried-set Q, we won't erase too much from. “tentative set ∩ Q” in subsequent iterations. ⇒ thus, “final set ∩ Q” resembles our ...

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