optimization

问题 For the following code I want to optimize it using the pattern introduced in this solution. However, the problem is how to deal with referring to three nested loops in a single statement. Moreover, the condition is far different from that post. hint: W and S are NxN sparse double matrices. for i=1:N for j=1:N for k=1:N if W(j,k)~=0 temp(k)=S(i,j)-S(i,k); end end sum_temp=max(temp)+sum_temp; temp=0; end B(i,i)=sum_temp; sum_temp=0; end 回答1: In this situation I would opt against fully

问题 I am trying to make a game similar to agar.io. I have a blob which is controlled by the player. It can move around and eat food. There is a different class for food as well. 200 instances of the food class is created: def spawn_food(self): if len(self.foods) <= 200: self.foods.append(Food()) Everything upuntil now is working fine, however, my entire computer hangs if i try to run the collision detection between all the foods and the blob. This is the code: def ate(self): for food in self

问题 Want to know which method is faster in android. Just for knowledge. TextView t; t = (TextView) findViewById(R.id.TextView_ID); int number=5; t.setText(""+number); or t.setText(String.valueOf(number)); 回答1: String.valueOf(int) is fastest, as most direct. However this is a micro-optimisation. The compiler probably does optimize this itself. "" + number would do a conversion too, and a string concatenation. Theoretically. 回答2: t.setText(String.valueOf(number)) is measurably faster . Here is

问题 I am trying to solve the following linear system using optimize.root AX = b With the following code. A = [[0,1,0],[2,1,0],[1,4,1]] def foo(X): b = np.matrix([2,1,1]) out = np.dot(A,X) - b return out.tolist() sol = scipy.optimize.root(foo,[0,0,0]) I know that I can simply use the numpy.linalg.solve to do this easily. But I am actually trying to solve a non linear system that is in matrix form. See my question here. So I need to find a way to make this method work. To do that I am trying to

问题 I have found this (personally) convenient answer that fits my needs: https://stackoverflow.com/a/6713782/2678218 But since I am using TypeScript , I can have something like this with Generics : private equals<T>(x: T, y: T) { if (x === y) { return true; // if both x and y are null or undefined and exactly the same } else if (!(x instanceof Object) || !(y instanceof Object)) { return false; // if they are not strictly equal, they both need to be Objects } else if (x.constructor !== y

问题 I'm using Google Or-Tools to solve a Traveling Salesman Problem by using this example (basically I just replaced the distances matrix with mine). As in the example, I set data['depot'] = 0 . For my application it is not important to return to the first node at the end of the path. I can remove the last edge from the solution but I wonder that if I could remove this constraint altogether it might find a better path overall. 回答1: Make sure the distance from all nodes to 0 (the depot) is null.

问题 I'm using Google Or-Tools to solve a Traveling Salesman Problem by using this example (basically I just replaced the distances matrix with mine). As in the example, I set data['depot'] = 0 . For my application it is not important to return to the first node at the end of the path. I can remove the last edge from the solution but I wonder that if I could remove this constraint altogether it might find a better path overall. 回答1: Make sure the distance from all nodes to 0 (the depot) is null.

问题 The way I'm currently doing it (I'd prefer to get rid of the memcpy call): uint64_t integer; uint8_t string[8]; ... memcpy(&integer, &string, 8); //or swap the parameters Assuming integer array length to always be a multiple of 8 (64 bits total allocation) is a straight cast possible given compiler padding / alignment concerns? 回答1: There is absolutely no need to avoid or replace a memcpy() call if you're striving for optimization. Every modern optimizing compiler won't emit a call and

问题 I'm new to gekko and APM, I'm trying to solve a problem but the solution seems to get infeasible with a new equation in a binary variable that should be viable. Here's the simplified APM model: Model Variables int_v1 = 0, >= 0 int_v2 = 0, <= 1, >= 0 v3 = 0 v4 = 0 v5 = 0 v6 = 0 End Variables Equations (0+int_v1)>=100 v3=((3.15)*(int_v1)) v4>=((int_v2)*(300)) v5=(0+((int_v1)*(3.15))) minimize v6 End Equations Connections v3 = sum_1.x[1] v4 = sum_1.y v5 = sum_2.x[1] v6 = sum_2.y End Connections

问题 Clearly, fixed-width integral types should be used when the size is important. However, I read (Insomniac Games style guide), that "int" should be preferred for loop counters / function args / return codes / ect when the size isn't important - the rationale given was that fixed-width types can preclude certain compiler optimizations. Now, I'd like to make a distinction between "compiler optimization" and "a more suitable typedef for the target architecture". The latter has global scope, and