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Movie Title Year Distributor Notes Rev Formats 40 Something XXXtra 2 2004 Score DRO Aces in the Holes 2003 VCA LezOnly 4 DRO Adult Video News Awards 1992 1992 VCA Amateur American Style 6 1990 Arrow Productions American Garter 1993 VCA 1 DRO Anything That Moves 1992 VCA 2 DRO Barlow Affair 1991 Xcitement Video Facial 1 D Best of Raunch 1992 Coast To Coast Bizarre Traume 1991 Magma Chameleon 1989 VCA 4 DO Charm School Brats 2003 VCA Facial 1 DO Cheating Housewives 2 2013 Wildlife DRO Cheerleader Pink 2003 VCA Anal 1 DO Cheerleader School 2003 VCA Anal Facial 3 DR Cockalicious 2011 Wicked Pictures DRO Cumming Clean 1993 Flame Video Curse of the Cat Woman 1991 VCA Facial 8 DRO Deep Inside Ashlyn Gere 2002 VCA Facial 2 DO Deep Inside Centerfold Girls 1991 VCA Facial 1 DRO Deep Inside Racquel Darrian 1993 VCA 2 DRO Deep Inside Selena Steele 1993 VCA Desperate Mothers and Wives 3 2005 Evasive Angles Facial 1 DRO
Diary Of A MILF 1038 2006 naughtyamerica.com O Diary Of A MILF 1066 2006 naughtyamerica.com O Diary Of A MILF 1489 2007 naughtyamerica.com O Diary Of A MILF 988 2007 naughtyamerica.com O Enticed By My Friends Mom 1 2006 High Voltage DO Female Phone Fantasies 1988 Night Time Video NonSex Gangbang Girl 7 1992 Anabolic Video Facial Swallow 1 DRO Gangbang Girl 8 1992 Anabolic Video Facial DRO Girls Of Dyke Manor 1996 Lipstik Video LezOnly 1 O Girls Who Dig Girls 15 1989 Wet Video LezOnly Hate To See You Go 1991 VCA 1 DRO Hit That Ole Bitch 2008 Evasive Angles Facial DRO Horny Blowjob Babes 2000 VCA DRO Hot 40+ 28 2012 Channel 69 Squirt DO Last Resort 1990 VCA 1 DRO Lez Go Crazy 1992 Heatwave LezOnly Melted Pink 2002 VCA 1 DRO MILF Filth 2 2007 Hustler Video 4 DRO MILTF 19 2005 Heatwave Facial DO Moms a Cheater 3 2008 Incredible Digital DRO Moms a Cheater 4 2008 Incredible Digital Facial DRO Mother Fuckers 3 2006 Mayhem Facial 2 DRO Mr. Fun's Mondo Adventure! 1993 VCA My First Sex Teacher 4 2006 Pure Play Media 2 DR My First Sex Teacher 44 2006 naughtyamerica.com O My First Sex Teacher 83 2006 naughtyamerica.com O

My First Sex Teacher 9 2007 Pure Play Media CumSwap 2 DR My Friend's Hot Mom 15881 2013 naughtyamerica.com Facial O My Friend's Hot Mom 472 2005 naughtyamerica.com Facial O Nina Loves Ron 2012 Hustler Video Clip 1 DRO Oh What A Night 1990 VCA 1 DO Older Hairy Squirters 2 2008 Filmco Releasing DO P.O. Verted 7 2007 Juicy Entertainment 1 DRO PAW 2003 2003 Stevi's Secret O Playin' Dirty 1990 VCA 3 DRO Poke Her Face 2001 VCA DRO Positively Pagan 6 1993 Ataxi Raunch 2 1990 Coast To Coast 1 DRO Selena's Secrets 1991 Minky Productions Sirens 1991 VCA Facial 1 DRO Slick Honey 1989 VCA 1 DRO Sorority Sex Kittens 1 1993 VCA 4 DRO Sorority Sex Kittens 2 1992 VCA LezOnly 3 DRO Sorority Sex Kittens 6 2002 VCA Anal Facial 1 DO Steele Butt 1993 Arrow Productions Anal O Strokin' to the Oldies: Seka 2004 VCA DRO Tease 1990 VCA Facial 2 DRO Three Ways 7 1991 VCA O Total Titillation 2003 VCA DR Toy Box Lingerie Show 1991 Eye Shadow Productions NonSex True Legends of Adult Cinema: The Modern Video Era 1993 VCA Visualizer 1992 VCA Facial 2 DRO White Anal MILFs 2009 Evasive Angles DRO Wild and Innocent 1990 Coast To Coast O Wrestling Wildcats / Red Dress Riot 1995 Triumph NonSex Computational complexity theory focuses on classifying computational problems according to their inherent difficulty, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable 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 their computational complexity, i.e., the amount of resources needed to solve them, such as time and storage. Other measures of complexity 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. The P versus NP problem, one of the seven Millennium Prize Problems, is dedicated to the field of computational complexity.[1] 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, computational complexity theory 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. Contents 1 Computational problems 1.1 Problem instances 1.2 Representing problem instances 1.3 Decision problems as formal languages 1.4 Function problems 1.5 Measuring the size of an instance 2 Machine models and complexity measures 2.1 Turing machine 2.2 Other machine models 2.3 Complexity measures 2.4 Best, worst and average case complexity 2.5 Upper and lower bounds on the complexity of problems 3 Complexity classes 3.1 Defining complexity classes 3.2 Important complexity classes 3.3 Hierarchy theorems 3.4 Reduction 4 Important open problems 4.1 P versus NP problem 4.2 Problems in NP not known to be in P or NP-complete 4.3 Separations between other complexity classes 5 Intractability 6 Continuous complexity theory 7 History 8 See also 9 Works on Complexity 10 References 10.1 Citations 10.2 Textbooks 10.3 Surveys 11 External links Computational problems A traveling salesman tour through 14 German cities. Problem instances A computational problem can be viewed as an infinite collection of instances together with a solution for every instance. The input string for a computational problem is referred to as a problem instance, and should not be confused with the problem itself. In computational complexity theory, a problem refers to the abstract question to be solved. In contrast, an instance of this problem is a rather concrete utterance, which can serve as the input for a decision problem. For example, consider the problem of primality testing. The instance is a number (e.g., 15) and the solution is "yes" if the number is prime and "no" otherwise (in this case, 15 is not prime and the answer is "no"). Stated another way, the instance is a particular input to the problem, and the solution is the output corresponding to the given input. To further highlight the difference between a problem and an instance, consider the following instance of the decision version of the traveling salesman problem: Is there a route of at most 2000 kilometres passing through all of Germany's 15 largest cities? The quantitative answer to this particular problem instance is of little use for solving other instances of the problem, such as asking for a round trip through all sites in Milan whose total length is at most 10 km. For this reason, complexity theory addresses computational problems and not particular problem instances. Representing problem instances When considering computational problems, a problem instance is a string over an alphabet. Usually, the alphabet is taken to be the binary alphabet (i.e., the set {0,1}), and thus the strings are bitstrings. As in a real-world computer, mathematical objects other than bitstrings must be suitably encoded. For example, integers can be represented in binary notation, and graphs can be encoded directly via their adjacency matrices, or by encoding their adjacency lists in binary. Even though some proofs of complexity-theoretic theorems regularly assume some concrete choice of input encoding, one tries to keep the discussion abstract enough to be independent of the choice of encoding. This can be achieved by ensuring that different representations can be transformed into each other efficiently. Decision problems as formal languages A decision problem has only two possible outputs, yes or no (or alternately 1 or 0) on any input. Decision problems are one of the central objects of study in computational complexity theory. A decision problem is a special type of computational problem whose answer is either yes or no, or alternately either 1 or 0. A decision problem can be viewed as a formal language, where the members of the language are instances whose output is yes, and the non-members are those instances whose output is no. The objective is to decide, with the aid of an algorithm, whether a given input string is a member of the formal language under consideration. If the algorithm deciding this problem returns the answer yes, the algorithm is said to accept the input string, otherwise it is said to reject the input. An example of a decision problem is the following. The input is an arbitrary graph. The problem consists in deciding whether the given graph is connected or not. The formal language associated with this decision problem is then the set of all connected graphs — to obtain a precise definition of this language, one has to decide how graphs are encoded as binary strings. Function problems A function problem is a computational problem where a single output (of a total function) is expected for every input, but the output is more complex than that of a decision problem—that is, the output isn't just yes or no. Notable examples include the traveling salesman problem and the integer factorization problem. It is tempting to think that the notion of function problems is much richer than the notion of decision problems. However, this is not really the case, since function problems can be recast as decision problems. For example, the multiplication of two integers can be expressed as the set of triples (a, b, c) such that the relation a × b = c holds. Deciding whether a given triple is a member of this set corresponds to solving the problem of multiplying two numbers. Measuring the size of an instance To measure the difficulty of solving a computational problem, one may wish to see how much time the best algorithm requires to solve the problem. However, the running time may, in general, depend on the instance. In particular, larger instances will require more time to solve. Thus the time required to solve a problem (or the space required, or any measure of complexity) is calculated as a function of the size of the instance. This is usually taken to be the size of the input in bits. Complexity theory is interested in how algorithms scale with an increase in the input size. For instance, in the problem of finding whether a graph is connected, how much more time does it take to solve a problem for a graph with 2n vertices compared to the time taken for a graph with n vertices? If the input size is n, the time taken can be expressed as a function of n. Since the time taken on different inputs of the same size can be different, the worst-case time complexity T(n) is defined to be the maximum time taken over all inputs of size n. If T(n) is a polynomial in n, then the algorithm is said to be a polynomial time algorithm. Cobham's thesis argues that a problem can be solved with a feasible amount of resources if it admits a polynomial time algorithm. Machine models and complexity measures Turing machine Main article: Turing machine An illustration of a Turing machine A Turing machine is a mathematical model of a general computing machine. It is a theoretical device that manipulates symbols contained on a strip of tape. Turing machines are not intended as a practical computing technology, but rather as a general model of a computing machine—anything from an advanced supercomputer to a mathematician with a pencil and paper. It is believed that if a problem can be solved by an algorithm, there exists a Turing machine that solves the problem. Indeed, this is the statement of the Church–Turing thesis. Furthermore, it is known that everything that can be computed on other models of computation known to us today, such as a RAM machine, Conway's Game of Life, cellular automata or any programming language can be computed on a Turing machine. Since Turing machines are easy to analyze mathematically, and are believed to be as powerful as any other model of computation, the Turing machine is the most commonly used model in complexity theory. Many types of Turing machines are used to define complexity classes, such as deterministic Turing machines, probabilistic Turing machines, non-deterministic Turing machines, quantum Turing machines, symmetric Turing machines and alternating Turing machines. They are all equally powerful in principle

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