Using the decision problem, PATH in order to solve the optimization problem, SHORTEST-PATH in polynomial time

So, if I were using a black-box decision algorithm, PATH in which I could say, “does a path of weight k exist in this graph from S to V“, problably the typical way one would solve the optimization problem of shortest path is to start at k at zero and increment until PATH returns true. But since paths start at -inf shouldn’t we be starting there? (Thus making this optimization problem not solvable in polynomial time)

I know this isn’t right, I’m just having a hard time seeing which part I am missing here.

Nested for each statements optimization C#

Hi im trying to optimize this nested foreach and if statement function for work, Any ideas on how I can make it run faster? Thanks in advance <3

My code is retrieving data from a SSMS SQL database with thousands of fields from multiple tables of data, moving them to lists and then looping through the lists to retrieve the data. it is moving all the data to a data grid view to be exported to an excel sheet

foreach (Calibrations calibration in Model.PostCalibrationsList)         {             if (calibration.Job_No == textBox1.Text)             {                 foreach (Instruments instrument in Model.InstrumentsList)                 {                     if (calibration.Inst_ID == instrument.Inst_ID)                     {                         foreach (Proc_InstInfo info in Model.Proc_InstInfoList)                         {                             foreach (ProcQRef qRef in Model.ProcQRefList)                             {                                 if (calibration.Inst_ID == instrument.Inst_ID)                                 {                                     if (instrument.Procedure == info.Proc_No.ToString() && instrument.Procedure == qRef.Procedure.ToString())                                     {                                         ex.dataGridView1.Rows.Add(calibration.Order_No, calibration.Inst_ID, instrument.Description, calibration.Cust_Ref, instrument.Manufacturer, instrument.Model_No, instrument.Serial_No, info.Comm1, calibration.Calibration_Required, qRef.StockCode, calibration.Cert_No, info.Proc_No);                                     }                                 }                             }                         }                     }                 }             }         } 

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Google Knowledge Panel Optimization

Hi! My family runs an outdoor living business (patios, pergolas, decks etc) and I do the SEO work as well. Can you share any tips on optimizing a website to appear on Google's Knowledge panel. I've already employed json ld markup and plan on updating GMP with new posts. Is that sufficient? Are there any known strategies out there that have been proven successful? Really want to get a knowledge panel for one of our main keywords. Thanks.

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What is MIUI optimization?

In MIUI (an Android ROM) there is an option in developer settings called MIUI optimization, what is the use of this option.When this option is enabled an app can’t be installed via USB (i.e. from android studio). If this option is disabled, does the performance of phone decrease?

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Optimization of Church-encoded booleans in System F

I can encode booleans in pure lambda calculus like this:

type Bool = forall t. t -> t -> t   true : Bool = \x y -> x false : Bool = \x y -> y 

Is there a procedure to optimize the following “dumb” function

dumb = \(b : Bool) -> b false false 


dumb = \(b : Bool) -> false 

I can imagine this is quite easy in Haskell with an ADT for example.

One way I can think of is counting the inhabitants of the type Bool (2) and then applying the function to both the inhabitants, discovering that the function will have the same result for any argument. This seems quite difficult to do in general though.