Answer:
Greedy is an algorithmic paradigm that builds up a solution piece by piece, always choosing the next piece that offers the most obvious and immediate benefit. Greedy algorithms are used for optimization problems. An optimization problem can be solved using Greedy if the problem has the following property: At every step, we can make a choice that looks best at the moment, and we get the optimal solution of the complete problem.
If a Greedy Algorithm can solve a problem, then it generally becomes the best method to solve that problem as the Greedy algorithms are in general more efficient than other techniques like Dynamic Programming. But Greedy algorithms cannot always be applied. For example, the Fractional Knapsack problem (See this) can be solved using Greedy, but 0-1 Knapsack cannot be solved using Greedy.
The following are some standard algorithms that are Greedy algorithms.
1) Kruskal’s Minimum Spanning Tree (MST): In Kruskal’s algorithm, we create an MST by picking edges one by one. The Greedy Choice is to pick the smallest weight edge that doesn’t cause a cycle in the MST constructed so far.
2) Prim’s Minimum Spanning Tree: In Prim’s algorithm also, we create an MST by picking edges one by one. We maintain two sets: a set of the vertices already included in MST and the set of the vertices not yet included. The Greedy Choice is to pick the smallest weight edge that connects the two sets.
3) Dijkstra’s Shortest Path: Dijkstra’s algorithm is very similar to Prim’s algorithm. The shortest-path tree is built up, edge by edge. We maintain two sets: a set of the vertices already included in the tree and the set of the vertices not yet included. The Greedy Choice is to pick the edge that connects the two sets and is on the smallest weight path from source to the set that contains not yet included vertices.
4) Huffman Coding: Huffman Coding is a loss-less compression technique. It assigns variable-length bit codes to different characters. The Greedy Choice is to assign the least bit length code to the most frequent character. The greedy algorithms are sometimes also used to get an approximation for Hard optimization problems. For example, the Traveling Salesman Problem is an NP-Hard problem. A Greedy choice for this problem is to pick the nearest unvisited city from the current city at every step. These solutions don’t always produce the best optimal solution but can be used to get an approximately optimal solution.
<span>You can align controls in the report design window using the align button on the report design tools arrange tab.</span> This tab is used to apply different types of formatting to reports in Access. The user<span> can change one type of control layout to another, can remove controls from layouts so that she/he can position the controls wherever you want on the report.</span>
Answer:
Try going to your settings and allow output camera
Explanation:
Answer:
The class of language the machines recognise is Regular Language (See Explanation Below)
Explanation:
Given
Form δ : Q × Γ → Q × Γ × {R, S}
From the above transition form, it can be seen that the machine cannot read square symbols passed to it.
Regarding the square the machine is currently reading, there are multiple movement of S and it shouldn't be so because any number of the multiple movement can be simulated by exactly one movement of S.
As stated earlier that sequence of moves can be simulated by just one movement.
Let R = the movement
This means the machine can only use right move efficiently.
With this, we can say that the machine only read input string.
This is a characteristic of DFA (Deterministic Finite Automata).
With this, we can conclude that some DFAs will simulate the Turing machine and that they only read regular language.
Answer:
or formatting marks are characters for content designing in word processors, which aren't displayed at printing. It is also possible to customize their display on the monitor. The most common non-printable characters in word processors are pilcrow, space, non-breaking space, tab character etc.
Explanation:
