Here’s a problem that occurs in automatic program analysis. For a set of variablesx1,......,xn, you are given some equality constraints, of the form “ xi=xj” and some disequality constraints, of the form “ xixj.” Is it possible to satisfy all of them?

For instance, the constraints.

x1=x2,x2=x3,x3=x4,x1x4

cannot be satisfied. Give an efficient algorithm that takes as input m constraints over n variables and decides whether the constraints can be satisfied.

Short Answer

Expert verified

Yes, it is possible to satisfy all of them and its efficient algorithm that takes as input and constraints over variables is given below.

Step by step solution

01

Automatic program analysis.

The Automatic program analysis program analysis is the process of automatically analyzing the behavior of computer programs regarding a property such as correctness, robustness, safety and likeness. Automatic program analysis program analysis focuses on two major areas that are program optimization and program correctness.

02

Efficient algorithm for satisfy the condition.

A problem where you maximize or minimize a real function by systematically choosing input values from an allowed set and computing the value of the function and a set of variables x1,......,xn, with some equality constraints of the form “xi=xj” and some disequality constraints, of the form “ xi=xj.” For that instance, the constraints is given in the question is,

x1=x2,x2=x3,x3=x4,x1x4

Cannot be satisfied. And that will takes as input m constraints over n variables.

Let’s take input: m and n, which contain variables as x and y also contains constraints over these all variable, the program analysis using constraints is divisible into constraint generation and constraint resolution. Constraint generation produces constraints from a program text that give a declarative speciation of the desired information about the program. Constraint resolution (i.e., solving the constraints) then computes this desired information. In the author’s view, the constraint-based analysis paradigm is appealing for three primary reasons constraints separate speciation from implementation, constraints yield natural speculations and constraints enable sophisticated implementations.

Here, set of variablesx1,......,xn, with some equality constraints of the form “ xi=xj” and some disequality constraints, of the form isxixj. An inequality constraint can be either active, ε-active, violated, or inactive at a design point. On the other hand, an equality constraint is either active or violated at a design point.

For equality constraints of the form the condition must be follows: “xi=xj”.

And for disequality constraints, of the form the condition must be follows: “inequality constraints”.

And by the definition of the equality constraints and from the disequality constraints these above both the conditions satisfy the instances which are showing in the equation.

x1=x2,x2=x3,x3=x4,x1x4, hence it is shown in the equation that in the prefix constraints it follows equality constraints and in the end equation it shows the inequality constraints with the variable here and treated it as input variable x1=x2,x2=x3,x3=x4,. follows equality constraints. And herex1x4is inequality constraints with this condition .Hence, it satisfy the constraints.

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Most popular questions from this chapter

Is there a faster way to compute the nth Fibonacci number than by fib2 (page 4)? One idea involves matrices.

We start by writing the equations F1=F1 and F2=F0+F1 in matrix notation:


role="math" localid="1659767046297" (F1F2)=(0111).(F0F1).

Similarly,

F2F3=(0111).(F1F2)=(0111)2.(F0F1)

And in general

(FnFn+1)=(0111)n.(F0F1)

So, in order to compute Fn, it suffices to raise this 2×2 matrix, call it X, to the nth power.

a. Show that two 2×2matrices can be multiplied using 4additions and 8multiplications.

But how many matrix multiplications does it take to compute Xn?

b. Show that O(logn) matrix multiplications suffice for computing Xn. (Hint: Think about computing X8.)

Thus, the number of arithmetic operations needed by our matrix-based algorithm, call it fib3, is just O(logn), as compared to O(n)for fib2. Have we broken another exponential barrier? The catch is that our new algorithm involves multiplication, not just addition; and multiplications of large numbers are slower than additions. We have already seen that, when the complexity of arithmetic operations is taken into account, the running time offib2becomes O(n).

c. Show that all intermediate results of fib3 are O(n) bits long.


d. Let M(n)be the running time of an algorithm for multiplying n-bit numbers, and assume that M(n)=O(n2) (the school method for multiplication, recalled in Chapter 1, achieves this). Prove that the running time of fib3 is O(M(n)logn).


e. Can you prove that the running time of fib3 is O(M(n))? Assume M(n)=Θ(na)for some 1a2. (Hint: The lengths of the numbers being multiplied get doubled with every squaring.)


In conclusion, whether fib3 is faster than fib2 depends on whether we can multiply n-bit integers faster thanO(n2) . Do you think this is possible? (The answer is in Chapter 2.) Finally, there is a formula for the Fibonacci numbers:

role="math" localid="1659768125292" Fn=15(1+52)n15(152)n.

So, it would appear that we only need to raise a couple of numbers to the nth power in order to computeFn . The problem is that these numbers are irrational, and computing them to sufficient accuracy is nontrivial. In fact, our matrix method fib3 can be seen as a roundabout way of raising these irrational numbers to the nth power. If you know your linear algebra, you should see why. (Hint: What are the eigenvalues of the matrix X?)

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(b) What is the result of measuring the first qubit of |ψ?

(c) What is the result of measuring the second qubit after measuring the first qubit? (d) If the two qubits in state|ψ are very far from each other, can you see why the answer to (c) is surprising?

How long does the recursive multiplication algorithm (page 25) take to multiply an n -bit number by an m -bit number? Justify your answer.

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Input: An undirected tree T=(V,E).

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Consider the following game. A “dealer” produces a sequence s1···sn of “cards,” face up, where each card si has a value vi. Then two players take turns picking a card from the sequence, but can only pick the first or the last card of the (remaining) sequence. The goal is to collect cards of largest total value. (For example, you can think of the cards as bills of different denominations.) Assume n is even. (a) Show a sequence of cards such that it is not optimal for the first player to start by picking up the available card of larger value. That is, the natural greedy strategy is suboptimal. (b) Give an O(n2) algorithm to compute an optimal strategy for the first player. Given the initial sequence, your algorithm should precompute in O(n2) time some information, and then the first player should be able to make each move optimally in O(1) time by looking up the precomputed information.

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