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

A PTAS is an algorithm which takes an instance of an optimization problem and a parameter ε > 0 and, in polynomial time, produces a solution that is within a factor 1 + ε of being optimal (or 1 − ε for maximization problems). For example, for the Euclidean traveling salesman problem, a PTAS would produce a tour with length at most (1 + ε)*L*, with *L* being the length of the shortest tour.^{[1]} There exists also PTAS for the class of all dense constraint satisfaction problems (CSPs).^{[2]}^{[clarification needed]}

The running time of a PTAS is required to be polynomial in *n* for every fixed ε but can be different for different ε. Thus an algorithm running in time *O*(*n*^{1/ε}) or even *O*(*n*^{exp(1/ε)}) counts as a PTAS.

## Variants

### Deterministic

A practical problem with PTAS algorithms is that the exponent of the polynomial could increase dramatically as ε shrinks, for example if the runtime is O(*n*^{(1/ε)!}). One way of addressing this is to define the **efficient polynomial-time approximation scheme** or **EPTAS**, in which the running time is required to be *O*(*n*^{c}) for a constant *c* independent of ε. This ensures that an increase in problem size has the same relative effect on runtime regardless of what ε is being used; however, the constant under the big-O can still depend on ε arbitrarily. Even more restrictive, and useful in practice, is the **fully polynomial-time approximation scheme** or **FPTAS**, which requires the algorithm to be polynomial in both the problem size *n* and 1/ε. All problems in FPTAS are fixed-parameter tractable. Both the knapsack problem and bin packing problem admit an FPTAS.^{[3]}

Any strongly NP-hard optimization problem with a polynomially bounded objective function cannot have an FPTAS unless P=NP.^{[4]} However, the converse fails: e.g. if P does not equal NP, knapsack with two constraints is not strongly NP-hard, but has no FPTAS even when the optimal objective is polynomially bounded.^{[5]}

Unless P = NP, it holds that FPTAS ⊊ PTAS ⊊ APX.^{[6]} Consequently, under this assumption, APX-hard problems do not have PTASs.

Another deterministic variant of the PTAS is the **quasi-polynomial-time approximation scheme** or **QPTAS**. A QPTAS has time complexity for each fixed .

### Randomized

Some problems which do not have a PTAS may admit a randomized algorithm with similar properties, a **polynomial-time randomized approximation scheme** or **PRAS**. A PRAS is an algorithm which takes an instance of an optimization or counting problem and a parameter ε > 0 and, in polynomial time, produces a solution that has a *high probability* of being within a factor ε of optimal. Conventionally, "high probability" means probability greater than 3/4, though as with most probabilistic complexity classes the definition is robust to variations in this exact value (the bare minimum requirement is generally greater than 1/2). Like a PTAS, a PRAS must have running time polynomial in *n*, but not necessarily in ε; with further restrictions on the running time in ε, one can define an **efficient polynomial-time randomized approximation scheme** or **EPRAS** similar to the EPTAS, and a **fully polynomial-time randomized approximation scheme** or **FPRAS** similar to the FPTAS.^{[4]}

## As a complexity class

The term PTAS may also be used to refer to the class of optimization problems that have a PTAS. PTAS is a subset of APX, and unless P = NP, it is a strict subset. ^{[6]}

Membership in PTAS can be shown using a PTAS reduction, L-reduction, or P-reduction, all of which preserve PTAS membership, and these may also be used to demonstrate PTAS-completeness. On the other hand, showing non-membership in PTAS (namely, the nonexistence of a PTAS), may be done by showing that the problem is APX-hard, after which the existence of a PTAS would show P = NP. APX-hardness is commonly shown via PTAS reduction or AP-reduction.

## References

**^**Sanjeev Arora, Polynomial-time Approximation Schemes for Euclidean TSP and other Geometric Problems, Journal of the ACM 45(5) 753–782, 1998.**^**Arora, S.; Karger, D.; Karpinski, M. (1999), "Polynomial Time Approximation Schemes for Dense Instances of NP-Hard Problems",*Journal of Computer and System Sciences*,**58**(1): 193–210, doi:10.1006/jcss.1998.1605**^**Vazirani, Vijay (2001).*Approximation algorithms*. Berlin: Springer. pp. 74–83. ISBN 3540653678. OCLC 47097680.- ^
^{a}^{b}Vazirani, Vijay V. (2003).*Approximation Algorithms*. Berlin: Springer. pp. 294–295. ISBN 3-540-65367-8. **^**H. Kellerer and U. Pferschy and D. Pisinger (2004).*Knapsack Problems*. Springer.CS1 maint: uses authors parameter (link)- ^
^{a}^{b}Jansen, Thomas (1998), "Introduction to the Theory of Complexity and Approximation Algorithms", in Mayr, Ernst W.; Prömel, Hans Jürgen; Steger, Angelika (eds.),*Lectures on Proof Verification and Approximation Algorithms*, Springer, pp. 5–28, doi:10.1007/BFb0053011, ISBN 9783540642015. See discussion following Definition 1.30 on p. 20.

## External links

- Complexity Zoo: PTAS, EPTAS, FPTAS
- Pierluigi Crescenzi, Viggo Kann, Magnús Halldórsson, Marek Karpinski, and Gerhard Woeginger,
*A compendium of NP optimization problems*– list which NP optimization problems have PTAS.