Mathematics Ratios Quiz

Questions and Answers

What is the primary function of the Hill Climbing algorithm in search methods?

To perform exhaustive search of all possible nodes.

To move to the best neighbor if it is better, else terminate. (correct)

To randomly select paths in the solution space.

To maximize the number of nodes visited in the search space.

Which method is suggested to address the issue of local optima during search?

Strictly following the gradient without deviation.

Using only deterministic methods for search.

Implementing randomization in the algorithms. (correct)

Avoiding exploration to maintain optimal paths.

In the context of search methods, what does exploitation refer to?

Choosing paths based solely on previous successful searches.

Randomly selecting nodes without considering their value.

Exploring multiple paths without evaluating their potential.

Following the gradient to reach local optima quickly. (correct)

What is the purpose of the beam width in Beam Search?

To look at the best options within a certain width at each level. Signup and view all the answers

What happens when a local optimum is reached in Hill Climbing?

The algorithm terminates and returns the current node. Signup and view all the answers

What is the expected behavior of Beam Search with a width of 2 when starting with 11111?

It will fail to solve the SAT problem. Signup and view all the answers

What is the h(n) value when 3 clauses are satisfied in the SAT problem?

3 Signup and view all the answers

What is the worst case time to solve a SAT problem with 50 variables?

1,125,899,906,842,624 Signup and view all the answers

How many neighbours does N1 generate when it flips 1 bit?

5 Signup and view all the answers

How long would it take to inspect a million nodes a second for a SAT problem with 100 variables?

12700000000000 centuries Signup and view all the answers

What does N2 represent in terms of bit flipping?

It flips 2 bits. Signup and view all the answers

In the comparison of SAT and TSP for 50 variables, what is the ratio of their solving times?

3 × 10^49 Signup and view all the answers

If N12 denotes a neighbourhood function that changes 1 or 2 bits, how many neighbours does it produce?

15 Signup and view all the answers

What is the time in hours to solve a SAT problem with 100 variables if inspecting a million nodes takes 1.27 × 10^24 seconds?

1.27 × 10^20 hours Signup and view all the answers

Which neighbourhood function produces the densest possible configuration?

N1-n Signup and view all the answers

Which of the following best describes the time complexity of the Traveling Salesman Problem (TSP) with brute force for 100 variables?

9.332621544 × 10^157 Signup and view all the answers

If the worst case time to solve SAT with 50 variables is given as 1,125,899,906,842,624, how would it be expressed in scientific notation?

1.12589990684 × 10^12 Signup and view all the answers

What problem-solving approach does Hill Climbing utilize in the context of the SAT problem?

Achieving the maximum number of satisfied clauses. Signup and view all the answers

What is the primary benefit of using iterated hill climbing in search methods?

It can escape from local optima. Signup and view all the answers

What does a Beam Search attempt to find in optimization problems?

A locally optimal solution. Signup and view all the answers

What is the estimated time in centuries to solve SAT with 100 variables by inspecting nodes at a million per second?

1.27 × 10^15 centuries Signup and view all the answers

What is the worst case time complexity for a SAT problem with 50 variables in terms of 2^N?

2^50 Signup and view all the answers

What does the variable 'h' represent in the context provided?

The number of satisfied clauses in the formula. Signup and view all the answers

Which expression is used to evaluate the success of a solution in the search method discussed?

F = (a Ú ¬b) ∧ (¬a Ú b Ú c) Signup and view all the answers

What is the proposed question regarding the start node in search methods?

What is the sanctity of the start node? Signup and view all the answers

Why might multiple starting points be advantageous in the search methods discussed?

They allow for exploration of varying solutions. Signup and view all the answers

What is the primary limitation of Hill Climbing as a search method?

It may get stuck in a local optimum. Signup and view all the answers

How does increasing the density of a neighbourhood affect Hill Climbing performance?

It increases the number of neighbours to inspect at each step. Signup and view all the answers

In the context of the Variable Neighbourhood Descent algorithm, what does the moveGen function represent?

Functions to generate neighbourhoods based on varying densities. Signup and view all the answers

What does the Best Neighbour algorithm do when it finds a better neighbour?

It moves to that neighbour and continues searching. Signup and view all the answers

What external factor is necessary for the termination of the Best Neighbour algorithm?

A defined termination criterion. Signup and view all the answers

What happens to the Best Neighbour algorithm once it reaches a better optimum?

It may quickly revert back to a local optimum. Signup and view all the answers

What does the 'inspect all neighbours' step entail in the Hill Climbing method?

Checking each neighbouring node for better options. Signup and view all the answers

Which statement about Hill Climbing and Local Optima is true?

It can potentially find better options even in local search. Signup and view all the answers

Study Notes

Ratios and Computational Complexity

Computational complexity is illustrated by the worst-case time to solve SAT (Boolean satisfiability) problems using 50 and 100 variables.
For 50 variables, SAT = 1,125,899,906,842,624 and TSP (Traveling Salesman Problem) = 3.041409320×10^64, resulting in a ratio of 3×10^49.
For 100 variables, SAT = 1.267650600×10^30 and TSP = 9.332621544×10^157, leading to a ratio of 10^127.
Assessing a million nodes per second equates to approximately 1.1 years for 50 variables and over 12.7 trillion centuries for 100 variables.

Escaping Local Optima

Defining heuristic functions that are both monotonic and well-behaved presents challenges in optimization tasks.
Algorithms must be developed to outperform simple Hill Climbing techniques.
The significance of exploration in search space as opposed to solely relying on exploitation of the gradient is highlighted.

Hill Climbing Algorithm

Hill Climbing progresses by moving to the best neighbor if it shows improvement; if not, the process terminates.
The algorithm does not require sorting, focusing instead on the best node to move towards, utilizing gradient information.

Exploration vs. Exploitation

Escaping local optima necessitates balancing exploration (deviating from current paths) and exploitation (moving along the gradient).
Exploration strategies enhance search capabilities by diversifying potential paths taken.

Beam Search

Beam Search examines multiple options at each level, defined by beam width.
With a beam width of 2, this search process can potentially miss solutions in narrower search paths.

Variable Neighbourhood Functions

Different neighborhood functions (N1 and N2) vary in neighbor count; N1 (1-bit flip) results in 5 neighbors, while N2 (2-bit flip) yields 10.
These neighborhoods can become increasingly dense, with N1-n representing all possible flips yielding 2^n neighbors, approaching a brute force style.

Impact on Hill Climbing Performance

Hill Climbing can become stuck in local optima where none of the neighboring states are advantageous.
Increased density in neighborhoods reduces this likelihood but also raises inspection costs, competing with brute force effectiveness.

Variable Neighbourhood Descent Algorithm

This approach employs multiple move generation strategies, aiming to optimize with sparser neighborhoods early on to facilitate "climbing" toward better solutions.

Best Neighbour Variation

The Best Neighbour algorithm continually moves to the best neighbor without strictly adhering to improvement, requiring an external termination condition.
It has potential for moving past local optima, although it may revert to previous states.

Iterated Hill Climbing

Engaging in multiple runs from various starting nodes can enhance the likelihood of finding optimal solutions, underscoring the limitations of a single starting point in complex searches.

Studying That Suits You

Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

Description

Test your knowledge on ratios with this quiz designed for mathematics students. Each question will assess your understanding of the relationship between numbers and their proportional values. Perfect for reinforcing key concepts in ratio analysis.