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Practice Exam
Practice Exam

Hidden Markov Models
Hidden Markov Models

Hidden Markov Models - Jianbo Gao's Home Page
Hidden Markov Models - Jianbo Gao's Home Page

Hidden Markov Models
Hidden Markov Models

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Journal Information Sheet - Society for Industrial and Applied

Complete Characterization of Near-Optimal Sequences for the Two
Complete Characterization of Near-Optimal Sequences for the Two

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expositions

... Suggested Topics for Presentation or Written Exposition Analyze these in detail, presenting or writing them up so that others can really understand in depth. Go beyond what is provided in the text. 3.1 Selection Sort and Bubble Sort: Consider when one would want to use these 3.3 Closest Pair and Con ...
Homework 1
Homework 1

... PK Ni N N N O max B logM/B B − i=1 B logM/B Ni , B I/Os, where Ni is the number of copies of the i-th element in the input set. (Hint: Use merge-sort and remove duplicates as soon as they are found. Analyze the algorithm by considering how many of the Ni copies of an element can be present after j m ...
3.1 Writing Equations
3.1 Writing Equations

Pseudocode Structure Diagrams
Pseudocode Structure Diagrams

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Problem Solving Partnerships using the SARA model
Problem Solving Partnerships using the SARA model

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S. Louridas and M. Rassias: Problem-Solving and

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Problem Set 1

Articles of Confederation
Articles of Confederation

due 4/01/2016 in class
due 4/01/2016 in class

Efficient Algorithms and Problem Complexity
Efficient Algorithms and Problem Complexity

lecture1212
lecture1212

Notes for Lecture 11
Notes for Lecture 11

Numerical Computations in Linear Algebra
Numerical Computations in Linear Algebra

Shortest Path
Shortest Path

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Session 33 – More Percent Problems Mentally Compute the

Hilbert`s problems and contemporary mathematical logic
Hilbert`s problems and contemporary mathematical logic

The focus is not on rote procedures, rote memorisation or tedious
The focus is not on rote procedures, rote memorisation or tedious

Chapter 2 – Multiply by 1 Digit Numbers
Chapter 2 – Multiply by 1 Digit Numbers

Topology/Geometry Jan 2016
Topology/Geometry Jan 2016

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Computational complexity theory



Computational complexity theory is a branch of the theory of computation in theoretical computer science and mathematics that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other. A computational problem is understood to be a task that is in principle amenable to being solved by a computer, which is equivalent to stating that the problem may be solved by mechanical application of mathematical steps, such as an algorithm.A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. The theory formalizes this intuition, by introducing mathematical models of computation to study these problems and quantifying the amount of resources needed to solve them, such as time and storage. Other complexity measures are also used, such as the amount of communication (used in communication complexity), the number of gates in a circuit (used in circuit complexity) and the number of processors (used in parallel computing). One of the roles of computational complexity theory is to determine the practical limits on what computers can and cannot do.Closely related fields in theoretical computer science are analysis of algorithms and computability theory. A key distinction between analysis of algorithms and computational complexity theory is that the former is devoted to analyzing the amount of resources needed by a particular algorithm to solve a problem, whereas the latter asks a more general question about all possible algorithms that could be used to solve the same problem. More precisely, it tries to classify problems that can or cannot be solved with appropriately restricted resources. In turn, imposing restrictions on the available resources is what distinguishes computational complexity from computability theory: the latter theory asks what kind of problems can, in principle, be solved algorithmically.
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