Knapsack problem ==>>
Given some items, pack the knapsack to get
the maximum total value. Each item has some
weight and some value. Total weight that we can
carry is no more than some fixed number W.
So we must consider weights of items as well as
their values.
Divide and Conquer Algorithms - D&C forms a distinct algorithm design technique in computer science, wherein a problem is solved by repeatedly invoking the algorithm on smaller occurrences of the same problem. Binary search, merge sort, Euclid's algorithm can all be formulated as examples of divide and conquer algorithms. Strassen's algorithm and Nearest Neighbor algorithm are two other examples.
Knapsack problem ==>>
Given some items, pack the knapsack to get
the maximum total value. Each item has some
weight and some value. Total weight that we can
carry is no more than some fixed number W.
So we must consider weights of items as well as
their values.
Divide and Conquer Algorithms - D&C forms a distinct algorithm design technique in computer science, wherein a problem is solved by repeatedly invoking the algorithm on smaller occurrences of the same problem. Binary search, merge sort, Euclid's algorithm can all be formulated as examples of divide and conquer algorithms. Strassen's algorithm and Nearest Neighbor algorithm are two other examples.
A single pass assembler scans the program only once and creates the equivalent binary program. The assembler substitute all of the symbolic instruction with machine code in one pass.
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It presents various approximation schemes including absolute approximation, epsilon approximation and also presents some polynomial time approximation schemes. It also presents some probabilistically good algorithms.
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A single pass assembler scans the program only once and creates the equivalent binary program. The assembler substitute all of the symbolic instruction with machine code in one pass.
The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers.
It presents various approximation schemes including absolute approximation, epsilon approximation and also presents some polynomial time approximation schemes. It also presents some probabilistically good algorithms.
This topic on matrix theory and linear algebra is fundamental. The focus is on subjects like systems of equations, vector spaces, determinants, eigenvalues, similarity, and positive definite matrices that will be helpful in other fields.
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CS 162 Fall 2015 Homework 1 Problems September 29, 2015 Timothy Johnson 1. Ex...parmeet834
CS 162 Fall 2015
Homework 1 Problems
September 29, 2015 Timothy Johnson
1. Exercise 2.2.4 on page 53 of Hopcroft et al. Give DFA’s accepting the following languages over the alphabet {0,1}. (a) The set of all strings ending in 0
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
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2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
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Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
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Introduction to the Theory of Computation, Winter 2003 A. Hevia and J. Mao Solution to Problem Set 1
1. CSE 105: Introduction to the Theory of Computation, Winter 2003 A. Hevia and J. Mao
Solution to Problem Set 1 January 21, 2003
Solution to Problem Set 1
1.4 a). L = {w|w begins with a 1 and ends with a 0}.
q0 q2
q3
q1
1
1
1
0, 1
0
0
0
d). L = {w|w has length at least 3 and its third symbol is a 0}.
q1 q3q2
0, 1
0
0, 1
1
q4
q0
0, 1 0, 1
). L = {w|w contains an even number of 0’s, or exactly two 1’s }.
q0 q2 q4
1
1
0, 1
q6
q5q3q1
1
1 1
1
0 00 0 0 0
1.5 a). L = {w|w ends with 00} with three states.
Notice that w only has to end with 00, and before the two zeros, there can be anything.
Therefore, we can construct the following NFA to recognize L:
0,1
0 0
q1 q2q0
1
2. CSE 105, Solution to Problem Set 1 2
1.10 b). We need to give an example of NFA M (and corresponding language C = L(M)) such
that, swapping the accept and non-accept states in M yields a NFA (say M ) that does
NOT recognize the complement of C. The example is the following: consider the language
C = { , 0 } recognized by the following NFA:
q1q0
0
Clearly, swapping the accept and non-accept states gives the following NFA M
q1q0
0
But the language recognized by M contains the empty word ( ) which already is in C = L(M).
Therefore, L(M ) cannot be the complement of L(M) (otherwise it wouldn’t accept ).
1.12 To transform an NFA into a DFA, we start with finding the -closure of the original start
state q0 and make E(q0) the new start state. A conveninent way to save work and avoid
mistakes is to compute transitions only for the new states appeared in previous computation,
starting from the new start state. This lazy strategy is proved to be correct and the proof
can be found in the first discussion section notes.
Second thing to bear in mind is that we’re always computing the -closure of the transitions.
Don’t forget that!
a). The -closure of the original start state remains the same for this NFA. Please refer
to Figure 1 for the final DFA.
States a b
1 1,2 2
1,2 1,2 1,2
2 ∅ 1
∅ ∅ ∅
a
b a,b
1
2
1
1,2
2
b
b
a
a,b a,b
a
Φ
Figure 1: Problem 1.12 (a)
3. CSE 105, Solution to Problem Set 1 3
b). Notice that for this NFA, the -closure of its original start state is no longer the same.
It is actually the new state {1, 2}. So we’ll start from here. Please refer to Figure 2 for the
final DFA.
States a b
1,2 1,2,3 ∅
1,2,3 1,2,3 2,3
∅ ∅ ∅
2,3 1,2 2,3
1 2
3
a
a a,b
b
1,2 1,2,3
2,3 Φ
a
b
a
a,bb
a
b
Figure 2: Problem 1.12 (b)
1.13 e). L = {w|w starts with 0 and has odd length, or starts with 1 and has even length}.
Solution: Almost directly from the definition of L:
0(00 ∪ 01 ∪ 10 ∪ 11)∗
∪ 1((00 ∪ 01 ∪ 10 ∪ 11)∗
(0 ∪ 1)) .
h). L = {w|w is any string except 11 and 111}.
Solution: ( ∪ 1) ∪ (0 ∪ 10 ∪ 110 ∪ 1110 ∪ 1111)(0 ∪ 1)∗.
Tip: How did we come up with this? First build the NFA or DFA that recognize the language
(or the complement of the language sought; recall that, given a DFA recognizing L, the DFA
recognizing L is given by swapping the accept and non-accept states in the DFA for L).
Then, by simple inspection (or using the NFA-to-RE transformation procedure) obtain the
corresponding RE.
For example, the DFA that recognizes L is:
q1 q3q2
1
0, 1
0
q4
q0
0
0
0, 1
1 1
(it’s not hard to see how to get to the regular expression from the above DFA).
j). L = {w|w contains at least two 0’s and at most one 1 }.
Solution: 000∗ ∪ (000∗1 ∪ 010 ∪ 100)0∗.
Tip: The DFA that recognizes L is:
4. CSE 105, Solution to Problem Set 1 4
q6
q0 q2 q4
0 0
1
q5q3q1
00
1
1
1
0, 1
0
0
1 1
1.14 a). We convert the regular expression (0 ∪ 1)∗000(0 ∪ 1)∗ into an NFA by following the steps
in Theorem 1.28 (shown in Figure 3).
Finally, for comparison, we also show an equivalent NFA built by observing patterns which
is much simpler (shown in Figure 4.)
b). Same steps as above for the regular expression (((00)∗(11))∪01)∗. Please refer to Figure 5.
1.16 a). We need to convert the NFA of Figure 6 (upper left automata) into a regular expression.
We must follow the usual procedure:
1. Transform the NFA into a GNFA
2. Remove states of the GNFA one by one. For example, we first remove state 1 and then
state 2.
3. When only the (single) start state and the (single) accept state are remaining, the
resulting regular expression is the regular expression labeling the last arrow. This is
a∗b(a ∪ ba∗b)∗.
The procedure is depicted in Figure 6.
b). We need to convert the NFA of Figure 7 (upper left) into a regular expression. We follow
the same procedure as before, which is depicted in Figure 7.
The equivalent regular expression is
∪ ((a ∪ b)a∗
b)(b ∪ a(a ∪ b))a∗
b)∗
( ∪ a) .
1.17 b). A2 = {www|w ∈ a, b∗}
Proof: Same as Example 1.40 on Pg. 81 of the Sipser book. c). A3 = {a2n
|n ≥ 0}.
(Here, a2n
means a string of 2n a’s)
Proof: Assume to the contrary that A3 is regular. By the Pumping Lemma, any string in
it longer than the pumping length should be pumpable. Let the pumping length be p. We
choose a2p
∈ A3 as the string that we will pump. Let w = xyz = a2p
. By condition 3 of
the lemma, |xy| ≤ p. This means that the pumping part, which has to be non-zero in length
cannot be of length greater than p.
5. CSE 105, Solution to Problem Set 1 5
(0U1)*
1
0
000
0 0 0
1
0 0 0
1
0
0
Figure 3: Problem 1.14 (a) NFA from following steps in Theorem 1.28
00 0
0,1 0,1
Figure 4: Problem 1.14 (a) Simpler NFA by inspection
6. CSE 105, Solution to Problem Set 1 6
0 0
0 0
1 1
10
0 0
1 1
0 0
1 1
10
Figure 5: Problem 1.14 (b)
b
a
a
b
f
s 1
2
b
a
a
b
2
1
f
s
2 a ∪ ba∗
b
a∗
b
f
s
a∗
b(a ∪ ba∗
b)∗
Figure 6: Problem 1.16 (a). Transforming a NFA into the equivalent RE. First, convert the original
NFA (first graph) into a GNFA (second graph); then remove state 1 (third graph) and state 2 (last
graph) until only states s and f are left.
7. CSE 105, Solution to Problem Set 1 7
2
f
s
3
b
b
a1
a
a ∪ b
2
3
b
b
a1
a, b
a
2
f
s
∪ a
3
b
a
a ∪ b
b ∪ a(a ∪ b)
f
s
∪ a
3
(a ∪ b)a∗
b
(b ∪ a(a ∪ b))a∗
b
f
s
∪ (a ∪ b)a∗
b((b ∪ a(a ∪ b))a∗
b)∗
( ∪ a)
Figure 7: Problem 1.16 (b) Transforming a NFA into the equivalent RE. First, convert the given
NFA (upper left) into a GNFA (upper middle); then in sequence remove state 1 (upper right), 2
(lower left), and 3 (lower right) until only states s and f are left.
Let the length of this part be q s.t. 0 ≤ q ≤ p. By Clause 1 of the Pumping Lemma:
∀k ∈ N, ∃n ∈ N s.t. 2n = 2p + kq
Let us consider the case where k = 1. By the Pumping Lemma, there must be an n s.t.
2n = 2p + q where 0 < q ≤ p.
Now we know that for ∀m ∈ N, 2m + m < 2m+1. Since q < p, it follows that 2p + q < 2p+1.
Hence it is not possible that the string we get by one round of pumping be a member of
A3. That is, there is a ”long enough” string in A3 that cannot be pumped. Hence A3 is not
regular.
In general, languages that involve more than ”linear” growth are never regular.
1.23 c). Consider L = { anbm : n = m }. Prove this language is not regular by using the Pumping
Lemma.
Proof: Assume by contradiction that L is regular. Then, by PL, we know that there exists
a pumping length p > 0 such that any word w ∈ L, |w| ≥ p can be partitioned as xyz = w
(with |xy| ≤ p and |y| > 0) in such a way that, for any i ≥ 0, the word xyiz also belongs to
L.
Notice that reaching a contradiction may be tricky in this case, since it’s not enough to
show that there exists a partition xyz = w such that w cannot be “pumped” (recall that by
“pumping a word w = xyz” we mean considering words of the form xyiz for i ≥ 0). Instead,
we need to exhibit a word w ∈ L such that, any possible partition xyz = w, w cannot be
pumped without falling out language L.
Most choices of words do not work since they can be pumped. Magically, (hopefully, by the
end of the problem you’ll see why) we use the word 0p1p+p!, where p! = p · (p − 1) · · · 2 · 1.
Clearly |w| ≥ p. Moreover, any partition of w into xyz must be such that y comprises only 0 s
(since |xy| ≤ p). Then, it must be the case that y = 0k for some k 0 < k ≤ p (since |y| > 0).
Now, we consider the word w = xyiz, for some i ≥ 0 which we leave unspecified for now.
8. CSE 105, Solution to Problem Set 1 8
The word w equals xyiz = 0p+(i−1)k1p+p!. We want to prove that for any value of k (that is,
any possible y and thus, any possible partition) there exists a value of i ≥ 0 which causes w
to have the same number of 0 s and 1 s: n = p + (i − 1)k = p + p! = m. This contradicts the
condition n = m for words in L.
Indeed, by solving p + (i − 1)k = p + p! we get i = p!
k + 1. So, for any value of k (recall that
0 < k ≤ p) p!
k + 1 will be a positive integer and thus, there exists a value i ≥ 0 (namely
i = p!
k + 1) such that w ∈ L. Nevertheless, by PL, w ∈ L. We’ve got a contradiction.
Alternative solution:
We can also show that this language is not regular by using closure properties of regular
languages.
By contradiction, assume L is regular. Then L is also regular. Let’s see how L looks like: L
is the language of all the words that either (a) are of the form anbm, where n = m, or (b)
contain the substring ba (which is not allowed in L). Therefore, L = { anbn : n ≥ 0 } ∪ { w :
w = (a ∪ b)∗ba(a ∪ b)∗ }. And thus,
L { w : w = (a ∪ b)∗
ba(a ∪ b)∗
} = { an
bn
: n ≥ 0 } .
The left hand side of the last expression is regular because the set-minus operation is regular
(recall that A B = A ∩ B). However, the right hand side is not. We’ve got a contradiction.
d). Show that the language L = {w | w ∈ {0, 1}∗ is not palindrome }.
Proof: Recall that a word w is palindrome if w = wR, where wR is the word formed by
reversing the symbols in w (eg. if w = 010111, then wR = 111010). For example w =
0100110010 is palindrome.
As always, we prove it by contradiction. Assume L is regular. Then the language complement
of L, say L = L = {w | w ∈ {0, 1}∗ is palindrome } is also regular.
If L is regular, then by pumping lemma, there exits an integer p > 0 (the pumping length)
such that any word w ∈ L , |w| ≥ p can be partitioned as xyz = w (with |xy| ≤ p and |y| > 0)
in such a way that, for any i ≥ 0, the word xyiz also belongs to L .
However, consider the word w = 0p10p. Clearly, |w| = 2p + 1 so it satisfies the condition
|w| ≥ p of the theorem. However, any valid partition of w into words x, y, z (valid in the
sense that w = xyz, |y| > 0, and |xy| ≤ p) must be such that y = 0k for some integer k > 0,
(since |xy| ≤ p, string y can’t contain the middle 1) and then x = 0p−k, and z = 10p. If we
form the word w = xy0z (that is, take i = 0). then w = 0p−k 10p = 0p−k10p which cannot
be palindrome since p − k < p. Thus, w ∈ L , which contradicts the result of the pumping
lemma.