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In computer science and operations research, **exact algorithms** are algorithms that always solve an optimization problem to optimality.

**Computer science** is the study of processes that interact with data and that can be represented as data in the form of programs. It enables the use of algorithms to manipulate, store, and communicate digital information. A computer scientist studies the theory of computation and the practice of designing software systems.

Computer science is no more about computers than astronomy is about telescopes.

**Operations research**, or **operational research** (**OR**) in British usage, is a discipline that deals with the application of advanced analytical methods to help make better decisions. Further, the term **operational analysis** is used in the British military as an intrinsic part of capability development, management and assurance. In particular, operational analysis forms part of the Combined Operational Effectiveness and Investment Appraisals, which support British defense capability acquisition decision-making.

In mathematics and computer science, an **algorithm** is a set of instructions, typically to solve a class of problems or perform a computation. Algorithms are unambiguous specifications for performing calculation, data processing, automated reasoning, and other tasks.

Unless P = NP, an exact algorithm for an NP-hard optimization problem cannot run in worst-case polynomial time. There has been extensive research on finding exact algorithms whose running time is exponential with a low base.^{ [1] }^{ [2] }

**NP-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". A simple example of an NP-hard problem is the subset sum problem.

- Approximation-preserving reduction
- APX is the class of problems with some constant-factor approximation algorithm
- Heuristic algorithm
- PTAS - a type of approximation algorithm that takes the approximation ratio as a parameter

In computability theory and computational complexity theory, especially the study of approximation algorithms, an **approximation-preserving reduction** is an algorithm for transforming one optimization problem into another problem, such that the distance of solutions from optimal is preserved to some degree. Approximation-preserving reductions are a subset of more general reductions in complexity theory; the difference is that approximation-preserving reductions usually make statements on approximation problems or optimization problems, as opposed to decision problems.

In complexity theory the class **APX** is the set of **NP** optimization problems that allow polynomial-time approximation algorithms with approximation ratio bounded by a constant. In simple terms, problems in this class have efficient algorithms that can find an answer within some fixed multiplicative factor of the optimal answer.

In computer science, a **polynomial-time approximation scheme** (**PTAS**) is a type of approximation algorithm for optimization problems.

In computer science, the **time complexity** is the computational complexity that describes the amount of time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm are taken to differ by at most a constant factor.

In Operations Research, applied mathematics and theoretical computer science, **combinatorial optimization** is a topic that consists of finding an optimal object from a finite set of objects. In many such problems, exhaustive search is not tractable. It operates on the domain of those optimization problems, in which the set of feasible solutions is discrete or can be reduced to discrete, and in which the goal is to find the best solution. Some common problems involving combinatorial optimization are the travelling salesman problem ("TSP"), the minimum spanning tree problem ("MST"), and the knapsack problem.

In graph theory, an **independent set**, **stable set**, **coclique** or **anticlique** is a set of vertices in a graph, no two of which are adjacent. That is, it is a set *S* of vertices such that for every two vertices in *S*, there is no edge connecting the two. Equivalently, each edge in the graph has at most one endpoint in *S*. The size of an independent set is the number of vertices it contains. Independent sets have also been called internally stable sets.

In the mathematical discipline of graph theory, a **vertex cover** of a graph is a set of vertices such that each edge of the graph is incident to at least one vertex of the set. The problem of finding a **minimum vertex cover** is a classical optimization problem in computer science and is a typical example of an NP-hard optimization problem that has an approximation algorithm. Its decision version, the **vertex cover problem**, was one of Karp's 21 NP-complete problems and is therefore a classical NP-complete problem in computational complexity theory. Furthermore, the vertex cover problem is fixed-parameter tractable and a central problem in parameterized complexity theory.

In computer science and operations research, **approximation algorithms** are efficient algorithms that find approximate solutions to NP-hard optimization problems with **provable guarantees** on the distance of the returned solution to the optimal one. Approximation algorithms naturally arise in the field of theoretical computer science as a consequence of the widely believed P ≠ NP conjecture. Under this conjecture, a wide class of optimization problems cannot be solved exactly in polynomial time. The field of approximation algorithms, therefore, tries to understand how closely it is possible to approximate optimal solutions to such problems in polynomial time. In an overwhelming majority of the cases, the guarantee of such algorithms is a multiplicative one expressed as an approximation ratio or approximation factor i.e., the optimal solution is always guaranteed to be within a (predetermined) multiplicative factor of the returned solution. However, there are also many approximation algorithms that provide an additive guarantee on the quality of the returned solution. A notable example of an approximation algorithm that provides *both* is the classic approximation algorithm of Lenstra, Shmoys and Tardos for Scheduling on Unrelated Parallel Machines.

In computer science, **parameterized complexity** is a branch of computational complexity theory that focuses on classifying computational problems according to their inherent difficulty with respect to *multiple* parameters of the input or output. The complexity of a problem is then measured as a function of those parameters. This allows the classification of NP-hard problems on a finer scale than in the classical setting, where the complexity of a problem is only measured by the number of bits in the input. The first systematic work on parameterized complexity was done by Downey & Fellows (1999).

In the mathematical field of graph theory, a **cubic graph** is a graph in which all vertices have degree three. In other words, a cubic graph is a 3-regular graph. Cubic graphs are also called **trivalent graphs**.

In graph theory, a **dominating set** for a graph *G* = (*V*, *E*) is a subset *D* of *V* such that every vertex not in *D* is adjacent to at least one member of *D*. The **domination number** γ(*G*) is the number of vertices in a smallest dominating set for *G*.

In the mathematical discipline of graph theory, a **feedback vertex set** of a graph is a set of vertices whose removal leaves a graph without cycles. In other words, each feedback vertex set contains at least one vertex of any cycle in the graph. The **feedback vertex set problem** is an NP-complete problem in computational complexity theory. It was among the first problems shown to be NP-complete. It has wide applications in operating systems, database systems, and VLSI chip design.

In graph theory, a directed graph may contain directed cycles, a one-way loop of edges. In some applications, such cycles are undesirable, and we wish to eliminate them and obtain a directed acyclic graph (DAG). One way to do this is simply to drop edges from the graph to break the cycles. A **feedback arc set** (**FAS**) or **feedback edge set** is a set of edges which, when removed from the graph, leave a DAG. Put another way, it's a set containing at least one edge of every cycle in the graph.

In graph theory, the **clique-width** of a graph is a parameter that describes the structural complexity of the graph; it is closely related to treewidth, but unlike treewidth it can be bounded even for dense graphs. It is defined as the minimum number of labels needed to construct by means of the following 4 operations :

- Creation of a new vertex
*v*with label*i* - Disjoint union of two labeled graphs
*G*and*H* - Joining by an edge every vertex labeled
*i*to every vertex labeled*j*, where - Renaming label
*i*to label*j*

For a graph, a **maximum cut** is a cut whose size is at least the size of any other cut. The problem of finding a maximum cut in a graph is known as the **Max-Cut Problem.**

In computer science, **hardness of approximation** is a field that studies the algorithmic complexity of finding near-optimal solutions to optimization problems.

In computational complexity theory, a problem is **NP-complete** when it can be solved by a restricted class of brute force search algorithms and it can be used to simulate any other problem with a similar algorithm. More precisely, each input to the problem should be associated with a set of solutions of polynomial length, whose validity can be tested quickly, such that the output for any input is "yes" if the solution set is non-empty and "no" if it is empty. The complexity class of problems of this form is called NP, an abbreviation for "nondeterministic polynomial time". A problem is said to be NP-hard if everything in NP can be transformed in polynomial time into it, and a problem is NP-complete if it is both in NP and NP-hard. The NP-complete problems represent the hardest problems in NP. If any NP-complete problem has a polynomial time algorithm, all problems in NP do. The set of NP-complete problems is often denoted by **NP-C** or **NPC**.

In computational complexity theory, the **exponential time hypothesis** is an unproven computational hardness assumption that was formulated by Impagliazzo & Paturi (1999). The hypothesis states that 3-SAT cannot be solved in subexponential time in the worst case. The exponential time hypothesis, if true, would imply that P ≠ NP, but it is a stronger statement. It can be used to show that many computational problems are equivalent in complexity, in the sense that if one of them has a subexponential time algorithm then they all do.

The **EATCS--IPEC Nerode Prize** is a theoretical computer science prize awarded for outstanding research in the area of multivariate algorithmics. It is awarded by the European Association for Theoretical Computer Science and the International Symposium on Parameterized and Exact Computation. The prize was offered for the first time in 2013.

In graph theory, a branch of mathematics, a **chordal completion** of a given undirected graph G is a chordal graph, on the same vertex set, that has G as a subgraph. A **minimal chordal completion** is a chordal completion such that any graph formed by removing an edge would no longer be a chordal completion. A **minimum chordal completion** is a chordal completion with as few edges as possible.

**Fedor V. Fomin** is a professor of Computer Science at the University of Bergen. He is known for his work in algorithms and graph theory.

- ↑ Fomin, Fedor V.; Kaski, Petteri (March 2013), "Exact Exponential Algorithms",
*Communications of the ACM*,**56**(3): 80–88, doi:10.1145/2428556.2428575 . - ↑ Fomin, Fedor V.; Kratsch, Dieter (2010).
*Exact Exponential Algorithms*. Springer. p. 203. ISBN 978-3-642-16532-0.

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