In computer science, control flow or flow of control is the order in which individual statements, instructions or function calls of an imperative program are. If I want to check if some preconditions are present in order to run a java program what is best Do System. exit1 Or throw a RuntimeException in main to end the. Turn your routine work into modern art. IOGraph is an application that turns mouse movements into modern art. The idea is that you have the program running in the. Stack Overflow The Worlds Largest Online Community for Developers. Below you will find 150 project ideas. I have divided them into 10 different topic areas that I think the project may use the most. Keep in mind that a few of these. This section covers C Programming Examples on Graph Problems Algorithms. Every example program includes the description of the program, C code as well as output. Control flow Wikipedia. In computer science, control flow or flow of control is the order in which individual statements, instructions or function calls of an imperative program are executed or evaluated. The emphasis on explicit control flow distinguishes an imperative programming language from a declarative programming language. Within an imperative programming language, a control flow statement is a statement the execution of which results in a choice being made as to which of two or more paths to follow. For non strict functional languages, functions and language constructs exist to achieve the same result, but they are usually not termed control flow statements. A set of statements is in turn generally structured as a block, which in addition to grouping, also defines a lexical scope. Interrupts and signals are low level mechanisms that can alter the flow of control in a way similar to a subroutine, but usually occur as a response to some external stimulus or event that can occur asynchronously, rather than execution of an in line control flow statement. At the level of machine language or assembly language, control flow instructions usually work by altering the program counter. For some central processing units CPUs, the only control flow instructions available are conditional or unconditional branch instructions, also termed jumps. CategorieseditThe kinds of control flow statements supported by different languages vary, but can be categorized by their effect Continuation at a different statement unconditional branch or jumpExecuting a set of statements only if some condition is met choice i. Executing a set of statements zero or more times, until some condition is met i. Executing a set of distant statements, after which the flow of control usually returns subroutines, coroutines, and continuationsStopping the program, preventing any further execution unconditional haltPrimitiveseditA label is an explicit name or number assigned to a fixed position within the source code, and which may be referenced by control flow statements appearing elsewhere in the source code. A label marks a position within source code, and has no other effect. Line numbers are an alternative to a named label and used in some languages such as Fortran and BASIC, that are whole numbers placed at the start of each line of text in the source code. Languages which use these often impose the constraint that the line numbers must increase in value in each following line, but may not require that they be consecutive. For example, in BASIC In other languages such as C and Ada, a label is an identifier, usually appearing at the start of a line and immediately followed by a colon. For example, in C Success printfThe operation was successful. n The language ALGOL 6. ALGOL variants allowed whole numbers. The goto statement a combination of the English words go and to, and pronounced accordingly is the most basic form of unconditional transfer of control. Although the keyword may either be in upper or lower case depending on the language, it is usually written as gotolabel. The effect of a goto statement is to cause the next statement to be executed to be the statement appearing at or immediately after the indicated label. Goto statements have been considered harmful by many computer scientists, notably Dijkstra. SubroutineseditThe terminology for subroutines varies they may alternatively be known as routines, procedures, functions especially if they return results or methods especially if they belong to classes or type classes. In the 1. 95. 0s, computer memories were very small by current standards so subroutines were used mainlycitation needed to reduce program size. A piece of code was written once and then used many times from various other places in a program. Today, subroutines are more often used to help make a program that is more structured, e. If many programmers are working on one program, subroutines are one kind of modularity that can help divide the work. SequenceeditIn structured programming, the ordered sequencing of successive commands is considered one of the basic control structures, which is used as a building block for programs alongside iteration, recursion and choice. Minimal structured control floweditIn May 1. Bhm and Jacopini published an article1 in Communications of the ACM which showed that any program with gotos could be transformed into a goto free form involving only choice IF THEN ELSE and loops WHILE condition DO xxx, possibly with duplicated code andor the addition of Boolean variables truefalse flags. Later authors showed that choice can be replaced by loops and yet more Boolean variables. That such minimalism is possible does not mean that it is necessarily desirable after all, computers theoretically need only one machine instruction subtract one number from another and branch if the result is negative, but practical computers have dozens or even hundreds of machine instructions. What Bhm and Jacopinis article showed was that all programs could be goto free. Other research showed that control structures with one entry and one exit were much easier to understand than any other form,citation needed mainly because they could be used anywhere as a statement without disrupting the control flow. In other words, they were composable. Later developments, such as non strict programming languages and more recently, composable software transactions have continued this strategy, making components of programs even more freely composable. Some academics took a purist approach to the Bhm Jacopini result and argued that even instructions like break and return from the middle of loops are bad practice as they are not needed in the Bhm Jacopini proof, and thus they advocated that all loops should have a single exit point. This purist approach is embodied in the language Pascal designed in 1. The direct application of the Bhm Jacopini theorem may result in additional local variables being introduced in the structured chart, and may also result in some code duplication. 3 The latter issue is called the loop and a half problem in this context. 4 Pascal is affected by both of these problems and according to empirical studies cited by Eric S. Roberts, student programmers had difficulty formulating correct solutions in Pascal for several simple problems, including writing a function for searching an element in an array. A 1. 98. 0 study by Henry Shapiro cited by Roberts found that using only the Pascal provided control structures, the correct solution was given by only 2. Control structures in practiceeditMost programming languages with control structures have an initial keyword which indicates the type of control structure involved. clarification needed Languages then divide as to whether or not control structures have a final keyword. No final keyword ALGOL 6. C, C, Haskell, Java, Pascal, Perl, PHP, PLI, Python, Power. Shell. Such languages need some way of grouping statements together. ALGOL 6. 0 and Pascal begin. C, C, Java, Perl, PHP, and Power. Shell curly brackets. PLI DO. ENDPython uses indent level see Off side ruleHaskell either indent level or curly brackets can be used, and they can be freely mixed. Lua uses do. end. Final keyword Ada, ALGOL 6. Modula 2, Fortran 7. Mythryl, Visual Basic. The forms of the final keyword vary. Ada final keyword is end space initial keyword e. Find minimum s t cut in a flow network. In a flow network, an s t cut is a cut that requires the source s and the sink t to be in different subsets, and it consists of edges going from the sources side to the sinks side. The capacity of an s t cut is defined by the sum of capacity of each edge in the cut set. Source WikiThe problem discussed here is to find minimum capacity s t cut of the given network. Expected output is all edges of the minimum cut. For example, in the following flow network, example s t cuts are 0 ,1, 0, 2, 0, 2, 1, 2, 1, 3, etc. The minimum s t cut is 1, 3, 4, 3, 4 5 which has capacity as 1. We strongly recommend to read the below post first. Ford Fulkerson Algorithm for Maximum Flow Problem. Minimum Cut and Maximum Flow. Like Maximum Bipartite Matching, this is another problem which can solved using Ford Fulkerson Algorithm. This is based on max flow min cut theorem. The max flow min cut theorem states that in a flow network, the amount of maximum flow is equal to capacity of the minimum cut. See CLRS book for proof of this theorem. From Ford Fulkerson, we get capacity of minimum cut. How to print all edges that form the minimum cut The idea is to use residual graph. Following are steps to print all edges of minimum cut. Run Ford Fulkerson algorithm and consider the final residual graph. Find the set of vertices that are reachable from source in the residual graph. All edges which are from a reachable vertex to non reachable vertex are minimum cut edges. Print all such edges. Following is C implementation of the above approach. C. C program for finding minimum cut using Ford Fulkerson. Number of vertices in given graph. Returns true if there is a path from source s to sink t in. Also fills parent to store the path. GraphVV, int s, int t, int parent. Create a visited array and mark all vertices as not visited. V. memsetvisited, 0, sizeofvisited. Create a queue, enqueue source vertex and mark source vertex. Standard BFS Loop. V v. if visitedvfalse r. Graphuv 0. If we reached sink in BFS starting from source, then return. A DFS based function to find all reachable vertices from s. The function. marks visitedi as true if i is reachable from s. The initial values in. We can also use BFS to find reachable vertices. GraphVV, int s, bool visited. V i. if r. Graphsi Graph, i, visited. Prints the minimum s t cut. Cutint graphVV, int s, int t. Create a residual graph and fill the residual graph with. GraphVV r. Graphij indicates residual capacity of edge i j. V u. for v 0 v lt V v. Graphuv graphuv. V This array is filled by BFS and to store path. Augment the flow while tere is path from source to sink. Graph, s, t, parent. Find minimum residual capacity of the edhes along the. BFS. Or we can say find the maximum flow. INTMAX. for vt vs vparentv. Graphuv. update residual capacities of the edges and reverse edges. Graphuv pathflow. Graphvu pathflow. Flow is maximum now, find vertices reachable from s. V. memsetvisited, false, sizeofvisited. Graph, s, visited. Print all edges that are from a reachable vertex to. V i. for int j 0 j lt V j. Driver program to test above functions. Let us create a graph shown in the above example. VV 0, 1. 6, 1. Cutgraph, 0, 5. Python program for finding min cut in the given graph. Complexity EV3. Total augmenting path VE and BFS with adj matrix takes V2 times. This class represents a directed graph using adjacency matrix representation. ROW lengraph. COL lengraph0. Returns true if there is a path from source s to sink t in. Also fills parent to store the path. BFSself,s, t, parent. Mark all the vertices as not visited. Falseself. ROW. Create a queue for BFS. Mark the source node as visited and enqueue it. True. Standard BFS Loop. Dequeue a vertex from queue and print it. Get all adjacent vertices of the dequeued vertex u. If a adjacent has not been visited, then mark it. False and val 0. True. parentind u. If we reached sink in BFS starting from source, then return. True if visitedt else False. Returns tne min cut of the given graph. Cutself, source, sink. This array is filled by BFS and to store path. ROW. maxflow 0 There is no flow initially. Augment the flow while there is path from source to sink. BFSsource, sink, parent. Find minimum residual capacity of the edges along the. BFS. Or we can say find the maximum flow. Inf. s sink. whiles source. Add path flow to overall flow. ROW. for j in rangeself. COL. if self. graphij 0 and self. Create a graph given in the above diagram. Graphgraph. source 0 sink 5. Cutsource, sink. This code is contributed by Neelam Yadav. References http www. Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.