37 relations: Backpack, Branch and bound, Clique problem, Combinatorial auction, Combinatorial optimization, Compiler, Computer science, Concave function, Continuous knapsack problem, Convex function, Convex set, Decision problem, Diagonally dominant matrix, Discrete uniform distribution, Dynamic programming, Economics, Greedy algorithm, Heuristic, Knapsack problem, Lagrange multiplier, Lagrangian relaxation, Linear programming, Linearization, List of knapsack problems, Maxima and minima, NP-completeness, NP-hardness, Operations research, Optimization problem, Packing problems, Positive-definite matrix, Proof by exhaustion, Pseudo-polynomial time, Quadratic knapsack problem, Telecommunication, Transport network, Very-large-scale integration.

## Backpack

A backpack — also called bookbag, kitbag, knapsack, rucksack, rucksac, pack, sackpack or backsack — is, in its simplest form, a cloth sack carried on one's back and secured with two straps that go over the shoulders, but there can be variations to this basic design.

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## Branch and bound

Branch and bound (BB, B&B, or BnB) is an algorithm design paradigm for discrete and combinatorial optimization problems, as well as mathematical optimization.

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## Clique problem

In computer science, the clique problem is the computational problem of finding cliques (subsets of vertices, all adjacent to each other, also called complete subgraphs) in a graph.

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## Combinatorial auction

A combinatorial auction is a type of smart market in which participants can place bids on combinations of discrete items, or “packages”, rather than individual items or continuous quantities.

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## Combinatorial optimization

In applied mathematics and theoretical computer science, combinatorial optimization is a topic that consists of finding an optimal object from a finite set of objects.

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## Compiler

A compiler is computer software that transforms computer code written in one programming language (the source language) into another programming language (the target language).

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## Computer science

Computer science deals with the theoretical foundations of information and computation, together with practical techniques for the implementation and application of these foundations.

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## Concave function

In mathematics, a concave function is the negative of a convex function.

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## Continuous knapsack problem

In theoretical computer science, the continuous knapsack problem (also known as the fractional knapsack problem) is an algorithmic problem in combinatorial optimization in which the goal is to fill a container (the "knapsack") with fractional amounts of different materials chosen to maximize the value of the selected materials.

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## Convex function

In mathematics, a real-valued function defined on an ''n''-dimensional interval is called convex (or convex downward or concave upward) if the line segment between any two points on the graph of the function lies above or on the graph, in a Euclidean space (or more generally a vector space) of at least two dimensions.

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## Convex set

In convex geometry, a convex set is a subset of an affine space that is closed under convex combinations.

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## Decision problem

In computability theory and computational complexity theory, a decision problem is a problem that can be posed as a yes-no question of the input values.

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## Diagonally dominant matrix

In mathematics, a square matrix is said to be diagonally dominant if for every row of the matrix, the magnitude of the diagonal entry in a row is larger than or equal to the sum of the magnitudes of all the other (non-diagonal) entries in that row.

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## Discrete uniform distribution

In probability theory and statistics, the discrete uniform distribution is a symmetric probability distribution whereby a finite number of values are equally likely to be observed; every one of n values has equal probability 1/n.

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## Dynamic programming

Dynamic programming is both a mathematical optimization method and a computer programming method.

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## Economics

Economics is the social science that studies the production, distribution, and consumption of goods and services.

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## Greedy algorithm

A greedy algorithm is an algorithmic paradigm that follows the problem solving heuristic of making the locally optimal choice at each stage with the intent of finding a global optimum.

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## Heuristic

A heuristic technique (εὑρίσκω, "find" or "discover"), often called simply a heuristic, is any approach to problem solving, learning, or discovery that employs a practical method, not guaranteed to be optimal, perfect, logical, or rational, but instead sufficient for reaching an immediate goal.

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## Knapsack problem

The knapsack problem or rucksack problem is a problem in combinatorial optimization: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible.

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## Lagrange multiplier

In mathematical optimization, the method of Lagrange multipliers (named after Joseph-Louis Lagrange) is a strategy for finding the local maxima and minima of a function subject to equality constraints (i.e., subject to the condition that one or more equations have to be satisfied exactly by the chosen values of the variables).

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## Lagrangian relaxation

In the field of mathematical optimization, Lagrangian relaxation is a relaxation method which approximates a difficult problem of constrained optimization by a simpler problem.

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## Linear programming

Linear programming (LP, also called linear optimization) is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships.

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## Linearization

In mathematics, linearization is finding the linear approximation to a function at a given point.

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## List of knapsack problems

The knapsack problem is one of the most studied problems in combinatorial optimization, with many real-life applications.

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## Maxima and minima

In mathematical analysis, the maxima and minima (the respective plurals of maximum and minimum) of a function, known collectively as extrema (the plural of extremum), are the largest and smallest value of the function, either within a given range (the local or relative extrema) or on the entire domain of a function (the global or absolute extrema).

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## NP-completeness

In computational complexity theory, an NP-complete decision problem is one belonging to both the NP and the NP-hard complexity classes.

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## NP-hardness

NP-hardness (''n''on-deterministic ''p''olynomial-time hardness), in computational complexity theory, is the defining property of a class of problems that are, informally, "at least as hard as the hardest problems in NP".

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## Operations research

Operations research, or operational research in British usage, is a discipline that deals with the application of advanced analytical methods to help make better decisions.

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## Optimization problem

In mathematics and computer science, an optimization problem is the problem of finding the best solution from all feasible solutions.

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## Packing problems

Packing problems are a class of optimization problems in mathematics that involve attempting to pack objects together into containers.

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## Positive-definite matrix

In linear algebra, a symmetric real matrix M is said to be positive definite if the scalar z^Mz is strictly positive for every non-zero column vector z of n real numbers.

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## Proof by exhaustion

Proof by exhaustion, also known as proof by cases, proof by case analysis, complete induction, or the brute force method, is a method of mathematical proof in which the statement to be proved is split into a finite number of cases or sets of equivalent cases and each type of case is checked to see if the proposition in question holds.

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## Pseudo-polynomial time

In computational complexity theory, a numeric algorithm runs in pseudo-polynomial time if its running time is a polynomial in the numeric value of the input (the largest integer present in the input) — but not necessarily in the length of the input (the number of bits required to represent it), which is the case for polynomial time algorithms.

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## Quadratic knapsack problem

The quadratic knapsack problem (QKP), first introduced in 19th century, is an extension of knapsack problem that allows for quadratic terms in the objective function: Given a set of items, each with a weight, a value, and an extra profit that can be earned if two items are selected, determine the number of item to include in a collection without exceeding capacity of the knapsack, so as to maximize the overall profit.

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## Telecommunication

Telecommunication is the transmission of signs, signals, messages, words, writings, images and sounds or information of any nature by wire, radio, optical or other electromagnetic systems.

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## Transport network

A transport network, or transportation network is a realisation of a spatial network, describing a structure which permits either vehicular movement or flow of some commodity.

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## Very-large-scale integration

Very-large-scale integration (VLSI) is the process of creating an integrated circuit (IC) by combining hundreds of thousands of transistors or devices into a single chip.

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## Redirects here:

0-1 quadratic knapsack problem.

## References

[1] https://en.wikipedia.org/wiki/Quadratic_knapsack_problem