I want to put variables previously defined in PHP into an array and then export them to json

``````\$toJson = array();
\$arr = array_push(\$toJson, array(
'numero_casas' => \$n,
'token' => \$myToken,
'resumo_criptografico' => \$rc
));

echo \$toJson;

\$exitJson = json_encode(\$toJson);

echo \$exitJson;

fwrite(\$file, \$exitJson);
``````

mathematical optimization – FindMinimum, where variables are integers

I want to find the minimum of a function where the variables are integers. Here is a simple example of a minimum of work (my actual function is much more complicated).

``````ClearAll(f);
f({i1_, i2_}) := N(Total(1 + Sin(#) & /@ {i1, i2}));

FindMinimum({f({i1, i2}),
0 < i1 < 11 && 10 < i2 < 21 && {i1, i2} (Element) Integers}, {{i1,
5}, {i2, 15}})
``````

The result is an error message stating that integers are only allowed for linear programming.

Is there a technique to solve this kind of problem? Thank you very much

html – Add Variables in Javascript

Hi! I have the following code:

`````` \$(document).ready(function(){
\$(this).closest('.row').find('.inputQtd').val();
var qtd = Number(iqtd.val());
iqtd.val(qtd+1);
});
});
``````

The system is a shopping cart, so there are several products in the shopping cart.
My badgeCount variable should show the total number of items in the shopping cart. So if you've added 2 rice and 2 beans, my badgeCount variable should show 4.
In Java it's easy, I'm badgeCount + = amount. The total is saved. But I can not do that in Javascript.
Could someone help me? The picture is for a better explanation:

As you can see, there are 3 items in my shopping cart, and I want my badgeCount variable to store the sum of these 3 items (in this case 6).

(The badgeControl variable is displayed in the upper right corner next to the shopping cart icon.)

Graph Theory – Combinatorial system of equations with exponentially many equations in quadratic many variables

A particular question about graph theory (about the existence of graphs with a particular color inherited by perfect matches) can be translated into the satisfiability problem of a set of equations (formulated by Michael Engelhardt).

To let $$X_i$$ be indexed by an integer $$1 leq i leq n$$, and $$x_i in {0,1, ldots, c-1 }$$, We have variables $$omega_ {X_i, X_j, x_i, x_j} in mathbb {C}$$ (With $$omega_ {X_j, X_i, x_j, x_i} = omega_ {X_i, X_j, x_i, x_j}$$).

For a solid $$n$$ and $$c$$We ask if there are a number of $$omega_ {X_i, X_j, x_i, x_j}$$ This solves the following equation system sentence for each value of the variable $$x_i$$:

$$sum_ {m = 1} ^ {n!} prod_ {j = 1} ^ {n / 2} omega_ {X_ {P ^ {(m)} (2j-1)}, X_ {P ^ {( m)} (2j)}, x_ {P ^ {(m)} (2j-1)}, x_ {P ^ {(m)} (2j)}} = prod_ {i = 1} ^ {n- 1} delta_ {x_i, x_ {i + 1}}$$

Where $$P ^ {(m)} (k)$$ refers to the $$k$$entry in the $$m$$Permutation of $$1,2, lpoints, n$$,

The only known cases are $$c = 2$$ for everyone $$n$$, and $$c = 3$$ to the $$n = 4$$, A solution for $$c = 3$$ to the $$n = 4$$ is
$$omega_ {X_1, X_2,0,0} = frac {1} {8}, omega_ {X_3, X_4,0,0} = frac {1} {8}$$
$$omega_ {X_1, X_3,1,1} = frac {1} {8}, omega_ {X_2, X_4,1,1} = frac {1} {8}$$
$$omega_ {X_1, X_4,2,2} = frac {1} {8}, omega_ {X_2, X_3,2,2} = frac {1} {8}$$
and everything else $$omega_ {X_i, X_j, x_i, x_j} = 0$$,

Question 1: Does this equation have other solutions than $$(n, c = 2)$$ and $$(n = 4, c = 3)$$?

It is likely that the answer to this question is no, because

• For the special case $$omega_ {X_i, X_j, x_i, x_j} in mathbb {R _ +}$$Ilya Bogdanov has proved with graph theory methods that these are the only solutions.
• The number of equations to be fulfilled increases $$c ^ n$$while the number of free variables $$omega_ {X_i, X_j, x_i, x_j}$$ just grow as $$c ^ 2 frac {n (n-1)} {2}$$,

Nevertheless, no answer is known for any other case.

Question 2: Have you ever seen similar or related systems of equations or problems in general?

The source of the problem lies in quantum physics.

Common probability distributions with 5 dependent discrete variables

I'm curious if there is an equation that shows the difficulty of making a final distribution out of 5 dependent discrete variables. From the examples I found, the equations consist of two variables, and I am not sure how to formulate an equation to get 5 or more variables.

cv.complex variables – In the Dirichlet series for \$ 1 / zeta (s) \$ for real \$ s \$ and the zeros of zeta

To the $$Re (s)> 1$$, it is well known that
$$frac {1} { zeta (s)} = sum_ {n = 1} ^ { infty} frac { mu (n)} {n ^ s}$$ Where $$mu$$ denotes the Mobius function and $$zeta$$ is the Riemann zeta function. I have heard that if the series on the right has an analytical continuation REAL $$s_ {0} in (1/2, 1)$$, then $$zeta (s) neq 0$$ for each $$s$$ With$$Re (s) = s_0$$, But since all non-trivial zeros of the zeta function are complex, why does the analytic continuation of the mentioned series occur? real Values ​​of $$s$$ something to do with that complex Zeros?

Theming – TWIG template file passes variables

Basically, I create a bridge status monitor. I built this within views.

There are 3 main options: Open, Closed, Restrictions (conditional logic displays them in the form).

I managed to change the template of the view block to accept the output and change it and display it in certain HTML classes.

However, I noticed that the variable {output} does not move to another viewport block branch file. Therefore, I can not execute conditional logic on the output in this view.
& # 39;

bridge status

{% if output == "Open"%}

Bridge OPENNo restrictions exist

{% elseif output == "Restrictions"%}

Restrictions are available

{% elseif output == "Closed"%}

Bridge CLOSED

{% endif%}
{% if logged in%}
To edit
{% endif%} & # 39;

^ This is from the file that contains the output and as such works fine, but I need that variable in the other template.

I realize that I probably do not explain that very well, but let me know if you understand where I come from.

cv.complex variables – Simultaneous unification of Bers

I tried to understand Bers famous paper "Simultaneous Uniformization". For this post I have some questions. Any kind of help is appreciated.

To let $$S$$ and $$S ^ {& # 39;}$$ to be two abstract Riemann surfaces and $$f: S rightarrow S ^ {& # 39;}$$ a homeomorphism of limited eccentricity (it can be thought of as a quasi-conforming map, although QC maps by definition retain their orientation, but here $$f$$ may also be a reversal of orientation) and such a couple $$(S, (f), S ^ {& # 39;})$$ is called a coupled pair, where $$(f)$$ denotes the homotopy class of $$f$$, A coupled pair means $$textit {odd}$$ if $$f$$ is orientation reversal and $$textit {even}$$ if it is orientation-preserving. Now two coupled pairs $$(S, (f), S ^ {& # 39;})$$ and $$(S_ {1}, (f_ {1}), S ^ {& # 39;} _ {1})$$ are considered equivalent, if any $$textbf {compliant homeomorphisms}$$ $$h$$ and $$h ^ {& # 39;}$$ so that $$h (S) = S_ {1}$$. $$h ^ {& # 39;} (S ^ {& # 39;}) = S ^ {& # 39;} _ {1}$$ and $$(h ^ {& # 39;} circ f circ h ^ {- 1}) = (f_ {1})$$

$$textbf {Question 1:}$$ What does it mean to be a …? $$textbf {compliant homeomorphism}$$ between two abstract Riemann surfaces?

Second, suppose $$m$$ is a Beltrami differential on a Riemann surface $$S _ { circ}$$This Beltrami differential defines a metric $$S _ { circ}$$This metric in turn defines a compliant structure $$S _ { circ}$$ and this leads to a new complex structure (since there is a one-to-one correspondence between complex structures down to biholomorphism and conformal structures to isometry), name the resulting Riemann surface $$S _ { circ} ^ {m}$$ , That's why we have a new straight couple $$(S _ { circ} ^ {m}, (Id), S _ { circ})$$, Where $$(Id)$$ is the homotopy class of the identity card and an odd coupled pair $$(S _ { circ} ^ {m}, ( iota), bar {S _ { circ}})$$, Where $$iota$$ denotes the natural picture of $$S _ { circ}$$ on his reflection $$bar {S _ { circ}}$$,
$$bar {S _ { circ}}$$ is defined by replacing each local uniformization $$xi$$ on $$S _ { circ}$$ by $$bar { xi}$$,

$$textbf {Question 2:}$$ What is the explicit description of the map $$iota$$ ?

$$textbf {Question 3:}$$ Accept $$(S, (f), S ^ {& # 39;})$$ is an odd paired pair. How then can one show that this pair (as defined above) corresponds to one of the forms? $$(S _ { circ} ^ {m}, ( iota), bar {S _ { circ}})$$ ? (That can be assumed $$S$$ hyperbolic universal covering)

Probability theory – approximation for the sum of random variables

I have an exact CDF ($$F_ {X_i} (x)$$) and PDF ($$f_ {X_i} (x)$$) of a random variable $$X_i geq 0$$ which contains special functions. I needed an approach around that $$x = 0$$I have therefore derived an approximation as
$$F_ {X_i} (x) approximately one x log (a x)$$
$$f_ {X_i} (x) approx-a ( log (a x) +1)$$
Where $$a> 0$$and this CDF is tight.

However, I now need the CDF from
$$Y = sum_ {i = 1} ^ {N} X_i$$
Where $$X_i$$s are i.i.d.

I tried to tie this $$left (F_ {X_i} (x) right) ^ N$$ or $$left (F_ {X_i} (x / N) right) ^ N$$, I found that these are too loose for little ones $$N$$,

If someone has a good idea, deduce $$F_Y (y)$$ more precisely with small ones $$x$$, please share with me.

Statistics – Why is this inequality of truncated random variables correct?

Given an order $$X_k$$ from $$k$$ independent random variables reading book says I can form "truncated" random variables, i.

$$X_k (n) = X_k cdot textbf {1} _ { { omega: | X_k ( omega) | leq n }}$$

Where $$omega$$ means a result from the sample space. Define $$S_n$$ and $$has {S} _n$$ as:

$$S_n = X_1 + X_2 + … + X_n$$

$$has {S} _n = X_1 (n) + X_2 (n) + … X_n (n)$$

So that means that $$has {S} _n$$ is the sum of $$n$$ truncated random variables. We also define $$m_n$$ as:

$$m_n = mathbb {E} (X_1 (n))$$

Since the variable distributions (of $$X_k$$) are the same ones we have $$m_n = mathbb {E} (X_k (n))$$ for all $$k geq 1$$,

The book reads that the following inequality is "evident" where given $$epsilon> 0$$, we have:

$$P bigg ( big | frac {S_n} {n} – m_n big | geq epsilon bigg) leq P bigg ( big | frac { has {S} _n} {n} – m_n big | geq epsilon bigg) + P bigg ( has {S} _n neq S_n bigg)$$

But I'm not clear at all and I got lost. How can we have the above inequality? (ie we could get some big ones $$X_k$$ in the $$S_n$$so that the left term is much larger than the truncated right term).