Transcript Understanding Problem Hardness: Recent Developments and Directions

Understanding Problem Hardness:
Recent Developments and Directions
Bart Selman
Cornell University
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Introduction & Motivation
Computational Challenges in Planning, Reasoning,
Learning, and Adaptation.
What are the characteristics of challenging
computational problems?
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A Few Examples
Reasoning
many forms of deduction
abduction / diagnosis (e.g. de Kleer 1989)
default reasoning
(e.g. Kautz and Selman 1989)
Bayesian inference
(e.g. Dagum and Luby 1993)
Planning
domain-dependent and independent (STRIPS)
(e.g. Chapman 1987; Gupta and Nau 1991; Bylander1994)
Learning
neural net “loading” problem
Bayesian net learning
decision tree learning
(e.g. Blum and Rivest 1989)
An abundance of negative complexity results for
many interesting tasks.
Results often apply to very restricted formalisms,
and also to finding approximate solutions.
But worst-case, what about average-case?
Sometimes “surprising” results.
A closer look leads to new insights &
algorithms and solution strategies.
Outline
A --- “Early’’ results:
phase transitions & computational hardness
B ---
Current focus:
--- problem mixtures (tractable / intractable)
--- adding global structure
C ---
Future directions and prospects
--- modeling resource constraints
--- adaptive computing
--- deeper theoretical understanding
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A. “Early” Results
(‘90-’95)
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Example Domain: Satisfiability
SAT: Given a formula in propositional calculus, is there
an assignment to its variables making it true?
We consider clausal form, e.g.:
(a
b
c)
(
b
d)
(b
c
The canonical NP-complete problem.
(“exponential search space”)
e)
...
Variable Clause Size Model
Average Clause Length
Ib
m1/2
III
?
IV
1
II
Ic
Ia
m-1/2
1
m-2
m-1
Ratio of Clauses-to-Variables
Polynominal average time in regions:
Ia
Ib
Ic
II
III
Ð
Ð
Ð
Ð
Ð
Purdom 1987 - backtracking
Iwama 1989 - counting alg.
Brown and Purdom 1985 - pure literal rule
Franco 1991
Franco 1994
Open: region IV
Generating Hard Random Formulas
Key: Use fixed-clause-length model.
(Mitchell, Selman, and Levesque 1992; Kirkpatrick and
Selman 1994)
Critical parameter: ratio of the number of clauses
to the number of variables.
Hardest 3SAT problems at ratio = 4.25
Hardness of 3SAT
4000
50 var
40 var
20 var
DP Calls
3000
2000
1000
0
2
3
4
5
6
7
Ratio of Clauses-to-Variables
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Intuition
At low ratios:
few clauses (constraints)
many assignments
easily found
At high ratios:
many clauses
inconsistencies easily detected
The 4.3 Point
4000
50 var
40 var
20 var
DP Calls
3000
2000
1000
0
1.0
50% sat
Probability
0.8
0.6
0.4
0.2
0.0
2
3
4
5
6
7
Ratio of Clauses-to-Variables
Mitchell, Selman, and Levesque 1991
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Phase transition 2-, 3-, 4-, 5-, and 6-SAT
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Theoretical Status Of Threshold
Very challenging problem ...
Current status:
3SAT threshold lies between 3.003
and 4.6.
(Motwani et al. 1994; Broder et al. 1992;
Frieze and Suen 1996; Dubois 1990, 1997;
Kirousis et al. 1995; Friedgut 1997;
Archlioptas et al. 1999 / related work:
Beame, Karp, Pitassi, and Saks 1998;
Bollobas, Borgs, Chayes, Han Kim, and
Wilson 1999)
Phase transition and combinatorial problems is an
active research area with fruitful interactions
between computer science, physics (approaches
from statistical mechanics), and mathematics
(combinatorics / random structures).
Also, a close interaction between experimental and
theoretical work. (With experimental findings quite often
confirmed by formal analysis within months to a few years.)
Finally, relevance to applications via algorithmic
advances and notion of “critically constrained
problems”.
Consequences for Algorithm Design
Phase transition work instances led to
improvements in algorithms:
--- local search methods (e.g., GSAT / Walksat)
(Selman et al. 1992; 1996; Min Li 1996; Hoos 1998, etc.)
--- backtrack-style methods (Davis-Putnam and
variants / complete)
(Crawford 1993; Dubois 1994; Bayardo 1997; Zane 1998, etc.)
Progress
Propositional reasoning and search (SAT):
1990: 100 variables / 200 clauses (constraints)
1998: 10,000 - 100,000 variables / 10^6 clauses
Novel applications:
e.g. in planning (Kautz & Selman),
program debugging (Jackson),
protocol verification (Clarke), and
machine learning (Resende).
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B. Current Focus
--- mixtures of problem classes, e.g., 2-SAT
and 3-SAT (“moving between P and NP”)
the 2+p-SAT model
--- structured instances
perturbed quasi-group completion problems
Focus --- 1) mixtures: 2+p-SAT problem
mixture of binary and ternary clauses
p = fraction ternary
p = 0.0 --- 2-SAT / p = 1.0 --- 3-SAT
What happens in-between?
(Monasson, Zecchina, Kirkpatrick, Selman, and Troyansky,
Nature, to appear)
Phase Transition for 2+p-SAT
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Location Threshold
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Computational Cost
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Results for 2+p-SAT
p < ~ 0.41 --- model essentially behaves as 2-SAT
search proc. “sees” only binary constraints
smooth, continuous phase transition
p > ~ 0.41 --- behaves as 3-SAT (exponential scaling)
abrupt, discontinuous scaling
Many new, rigorous results (including scaling) by
Achlioptas, Bollobas, Borgs, Chayes, Han Kim,
and Wilson. (Next talk.)
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Consequences for Algorithm Design
1) Strategies that exploit tractable substructure
with propagation are most effective.
(consistent with the best empirically discovered
methods)
2) In addition, use early branching on critically
constrained variables.
(the “backbone variables” / suggests use of
clustering and statistical learning methods)
(Boyan and Moore 1998)
Focus --- 2) Structure
Proposal: study the influence of global
structure on problem hardness.
(Gomes and Selman 1997; 1998)
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Quasigroups
Defn.: a pair (Q, *) where Q is a set, and * is a binary
operation on Q such that
a*x=b; y*a=b
are uniquely solvable for every pair of elements a,b in Q.
The multiplication table of its binary operation defines a
latin square (i.e., each element of Q appears exactly once
in each row/column).
Example:
Quasigroup of order 4
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Quasigroup Completion Problem
(QCP)
Given a partial latin square, can it be
completed?
Example:
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Quasigroup Completion Problem
A Framework for Studying Search
NP-Complete (Colbourn 1983, 1984; Anderson 1985).
Has a regular global structure not found in
random instances.
Leads to interesting search problems when
structure is perturbed.
similar to e.g. structure found in the channel assignment problem
for cellular networks
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Computational Cost
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Consequences for Algorithm Design
On these structured problems, backtrack
search methods show so-called
heavy-tailed probability distributions.
(Gomes, Selman & Crato 1997, 1998).
Both very short and very long runs occur
much more frequent than one would expect.
Standard Distribution
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Heavy Tailed Cost Distribution
log( 1 - F(x) )
1
0.1
1
10
100
1000
10000
100000
log( Backtracks )
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Fringe of Search Tree
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Algorithmic Strategy:
Rapid Random Restarts.
Order of magnitude speedup.
(Gomes et al. 1998; 1999)
Related:
. Algorithm portfolios
(Huberman 1998; Gomes 1998)
. Universal strategies
(Ertel and Luby 1993; Alt et al. 1996)
Rapid Restarts --- Planning
log ( backtracks )
1000000
100000
10000
1000
1
10
100
1000
10000
100000
1000000
log( cutoff )
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Portfolio for heavy-tailed search
procedures (2-20 processors)
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C. Future directions and prospects
Modeling resource constraints &
user requirements / utility
should be possible to identify optimal
restart strategies, possibly adaptive
--- may need way of “measuring progress”
(Horvitz and Klein 1995; Gomes and Selman 1999)
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Adaptive Computing
combine statistical learning methods with
combinatorial search techniques.
first success: STAGE system for local search.
(Boyan and Moore 1998)
extension: train a planner on small instances
(Selman, Kautz, Huang 1999)
Deeper theoretical understanding
with continued interactions with experiments
and applications
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Summary
During the past few years, we have obtained a much
better understanding of the nature of
computationally hard problems.
Rich interactions between physics, computer
science and mathematics, and between theory,
experiments, and applications.
Clear algorithmic progress with room for future
improvements (possibly another level of scaling:
10^6 Boolean variables, 10^8 constraints. Further
applications.)