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Electronic Colloquium on Computational Complexity

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REPORTS > KEYWORD > SEARCH PROBLEMS:
Reports tagged with search problems:
TR10-128 | 15th August 2010
Scott Aaronson

The Equivalence of Sampling and Searching

Revisions: 1

In a sampling problem, we are given an input $x\in\left\{0,1\right\} ^{n}$, and asked to sample approximately from a probability
distribution $D_{x}$ over poly(n)-bit strings. In a search problem, we are given an input
$x\in\left\{ 0,1\right\} ^{n}$, and asked to find a member of a nonempty set
$A_{x}$ with high probability. ... more >>>


TR12-100 | 23rd July 2012
Tomas Jirotka

NP Search Problems

The thesis summarizes known results in the field of NP search problems. We discuss the complexity of integer factoring in detail, and we propose new results which place the problem in known classes and aim to separate it from PLS in some sense. Furthermore, we define several new search problems.

more >>>

TR12-101 | 10th August 2012
Oded Goldreich, Shafi Goldwasser, Dana Ron

On the possibilities and limitations of pseudodeterministic algorithms

We study the possibilities and limitations
of pseudodeterministic algorithms,
a notion put forward by Gat and Goldwasser (2011).
These are probabilistic algorithms that solve search problems
such that on each input, with high probability, they output
the same solution, which may be thought of as a canonical solution.
We consider ... more >>>


TR17-015 | 4th February 2017
Ilan Komargodski, Moni Naor, Eylon Yogev

White-Box vs. Black-Box Complexity of Search Problems: Ramsey and Graph Property Testing

Revisions: 2

Ramsey theory assures us that in any graph there is a clique or independent set of a certain size, roughly logarithmic in the graph size. But how difficult is it to find the clique or independent set? If the graph is given explicitly, then it is possible to do so ... more >>>


TR19-012 | 27th January 2019
Oded Goldreich

Multi-pseudodeterministic algorithms

Revisions: 1

In this work, dedicated to Shafi Goldwasser, we consider a relaxation of the notion of pseudodeterministic algorithms, which was put forward by Gat and Goldwasser ({\em ECCC}, TR11--136, 2011).

Pseudodeterministic algorithms are randomized algorithms that solve search problems by almost always providing the same canonical solution (per each input). ... more >>>


TR23-039 | 28th March 2023
Arkadev Chattopadhyay, Yogesh Dahiya, Meena Mahajan

Query Complexity of Search Problems

Revisions: 1

We relate various complexity measures like sensitivity, block sensitivity, certificate complexity for multi-output functions to the query complexities of such functions. Using these relations, we improve upon the known relationship between pseudo-deterministic query complexity and deterministic query complexity for total search problems: We show that pseudo-deterministic query complexity is at ... more >>>


TR24-010 | 19th January 2024
Noah Fleming, Stefan Grosser, Toniann Pitassi, Robert Robere

Black-Box PPP is not Turing-Closed

Revisions: 1

The complexity class PPP contains all total search problems many-one reducible to the PIGEON problem, where we are given a succinct encoding of a function mapping n+1 pigeons to n holes, and must output two pigeons that collide in a hole. PPP is one of the “original five” syntactically-defined subclasses ... more >>>


TR24-017 | 23rd January 2024
Siddhartha Jain, Jiawei Li, Robert Robere, Zhiyang Xun

On Pigeonhole Principles and Ramsey in TFNP

Revisions: 1

The generalized pigeonhole principle says that if tN + 1 pigeons are put into N holes then there must be a hole containing at least t + 1 pigeons. Let t-PPP denote the class of all total NP-search problems reducible to finding such a t-collision of pigeons. We introduce a ... more >>>


TR24-161 | 24th October 2024
Oded Goldreich

On defining PPT-search problems

We propose a new definition of the class of search problems that correspond to BPP.
Specifically, a problem in this class is specified by a polynomial-time approximable function $q:\{0,1\}^*\times\{0,1\}^*\to[0,1]$ that associates, with each possible solution $y$ to an instance $x$, a quality $q(x,y)$.
Intuitively, quality 1 corresponds to perfectly ... more >>>




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