Guest Column

2021 ◽  
Vol 52 (1) ◽  
pp. 47-69
Author(s):  
R. Pass ◽  
M. Venkitasubramaniam
Keyword(s):  
Recent Result ◽  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Average Case ◽  
Interactive Games ◽  
Case Complexity ◽  
Average Case Complexity ◽  
Probabilistic Polynomial Time

We review a study of average-case complexity through the lens of interactive puzzles- interactive games between a computationally bounded Challenger and computationally-bounded Solver/Attacker. Most notably, we use this treatment to review a recent result showing that if NP is hard-on-the-average, then there exists a sampleable distribution over only true statements of an NP language, for which no probabilistic polynomial time algorithm can find witnesses. We also discuss connections to the problem of whether average-case hardness in NP implies averagecase hardness in TFNP, or the existence of cryptographic one-way functions.

scholarly journals ADVICE FOR SEMIFEASIBLE SETS AND THE COMPLEXITY-THEORETIC COST(LESSNESS) OF ALGEBRAIC PROPERTIES

2005 ◽  
Vol 16 (05) ◽  
pp. 913-928 ◽  
Author(s):  
PIOTR FALISZEWSKI ◽  
LANE A. HEMASPAANDRA
Keyword(s):  
Computational Complexity ◽  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Worst Case ◽  
Advice Complexity ◽  
The Core ◽  
Case Complexity ◽  
Worst Case Complexity ◽  
Algebraic Properties

Informally put, the semifeasible sets are the class of sets having a polynomial-time algorithm that, given as input any two strings of which at least one belongs to the set, will choose one that does belong to the set. We provide a tutorial overview of the advice complexity of the semifeasible sets. No previous familiarity with either the semifeasible sets or advice complexity will be assumed, and when we include proofs we will try to make the material as accessible as possible via providing intuitive, informal presentations. Karp and Lipton introduced advice complexity about a quarter of a century ago.18 Advice complexity asks, for a given power of interpreter, how many bits of "help" suffice to accept a given set. Thus, this is a notion that contains aspects both of descriptional/informational complexity and of computational complexity. We will see that for some powers of interpreter the (worst-case) complexity of the semifeasible sets is known right down to the bit (and beyond), but that for the most central power of interpreter—deterministic polynomial time—the complexity is currently known only to be at least linear and at most quadratic. While overviewing the advice complexity of the semifeasible sets, we will stress also the issue of whether the functions at the core of semifeasibility—so-called selector functions—can without cost be chosen to possess such algebraic properties as commutativity and associativity. We will see that this is relevant, in ways both potential and actual, to the study of the advice complexity of the semifeasible sets.

Learning Importance of Preferences

10.29007/v68w ◽  
2018 ◽  
Author(s):  
Ying Zhu ◽  
Mirek Truszczynski
Keyword(s):  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Scoring Rules ◽  
Np Hard ◽  
Individual Preferences

We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decides all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples (also under the ranked Pareto rule) is NP-hard. We obtain similar results for the case of weighted profiles when positional scoring rules are used for aggregation.

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