restructured code

Author: ayansen

README.md

@@ -0,0 +1,49 @@
+# Code Logbook
+This repository contains solutions for standard programming problems structured by the algorithmic techniques or datastructures 
+used to solve them
+
+## Algorithm Techniques
+In mathematics and computer science, an algorithmic technique is a general approach for implementing a process or computation
+
+
+### Brute Force
+Brute Force search or exhaustive search, also known as generate and test, is a very general problem-solving technique and algorithmic paradigm that consists of systematically enumerating all possible candidates 
+for the solution and checking whether each candidate satisfies the problem's statement. An example of a brute force technique is backtracking.Backtracking is an algorithm where we 
+traverse all the possible paths to find the correct solution. 
+
+### Divide and Conquer
+The divide and conquer technique decomposes complex problems recursively into smaller sub-problems. 
+Each sub-problem is then solved and these partial solutions are recombined to determine the overall solution. 
+This technique is often used for searching and sorting.
+
+### Dynamic Programming
+Dynamic programming is a systematic technique in which a complex problem is decomposed recursively into smaller, overlapping sub-problems for solution.
+Dynamic programming stores the results of the overlapping sub-problems locally using an optimization technique called memoization
+
+
+### Greedy 
+A greedy approach begins by evaluating one possible outcome from the set of possible outcomes, and then searches locally for an improvement on that outcome.
+When a local improvement is found, it will repeat the process and again search locally for additional improvements near this local optimum. 
+A greedy technique is generally simple to implement, and these series of decisions can be used to find local optimums depending on where the search began. 
+However, greedy techniques may not identify the global optimum across the entire set of possible outcomes.
+
+
+### Graph Traversal
+Graph traversal is a technique for finding solutions to problems that can be represented as graphs.
+This approach is broad, and includes depth-first search, breadth-first search, tree traversal, and many specific variations that may include local optimizations and excluding search spaces that can be determined to be non-optimum or not possible. 
+These techniques may be used to solve a variety of problems including shortest path and constraint satisfaction problems.
+
+## Datastructures
+
+
+### HashMaps
+
+### Heap
+
+### List
+
+### Stack
+
+### Tree
+
+

src/main/java/algorithms/backtracking/FindLongestPathInMatrix.java renamed to src/main/java/algorithm/bruteforce/FindLongestPathInMatrix.java

@@ -1,4 +1,4 @@
-package algorithms.backtracking;
+package algorithm.bruteforce;
 
 import java.util.ArrayList;
 import java.util.List;

src/main/java/algorithms/backtracking/FindStringInDictionary.java renamed to src/main/java/algorithm/bruteforce/FindStringInDictionary.java

@@ -1,7 +1,6 @@
-package algorithms.backtracking;
+package algorithm.bruteforce;
 
 import java.util.ArrayList;
-import java.util.Arrays;
 import java.util.List;
 import java.util.Stack;
 

src/main/java/algorithms/backtracking/ResolveWildcardCharacter.java renamed to src/main/java/algorithm/bruteforce/ResolveWildcardCharacter.java

@@ -1,4 +1,4 @@
-package algorithms.backtracking;
+package algorithm.bruteforce;
 
 import java.util.ArrayList;
 import java.util.List;

src/main/java/algorithms/divideandconquer/LargestAndSmallestNumber.java renamed to src/main/java/algorithm/divideandconquer/LargestAndSmallestNumber.java

@@ -1,11 +1,12 @@
-package algorithms.divideandconquer;
+package algorithm.divideandconquer;
 
 /**
  * Created by aysen on 10/16/16.
  */
 
 
-import javafx.util.Pair;
+
+import algorithm.mathematicaloptimizations.lists.Pair;
 
 import java.util.List;
 

src/main/java/algorithms/dp/FibonacciSeries.java renamed to src/main/java/algorithm/dp/FibonacciSeries.java

@@ -1,4 +1,4 @@
-package algorithms.dp;
+package algorithm.dp;
 
 
 public class FibonacciSeries {

src/main/java/algorithms/dp/LongestCommonSubsequence.java renamed to src/main/java/algorithm/dp/LongestCommonSubsequence.java

@@ -1,4 +1,4 @@
-package algorithms.dp;
+package algorithm.dp;
 
 public class LongestCommonSubsequence {
 

src/main/java/algorithms/dp/LowestTravelCost.java renamed to src/main/java/algorithm/dp/LowestTravelCost.java

@@ -1,4 +1,4 @@
-package algorithms.dp;
+package algorithm.dp;
 
 import java.util.*;
 import java.util.stream.Collectors;

src/main/java/algorithms/dp/MaxSubMatrix.java renamed to src/main/java/algorithm/dp/MaxSubMatrix.java

@@ -1,4 +1,4 @@
-package algorithms.dp;
+package algorithm.dp;
 
 import java.util.Arrays;
 

src/main/java/algorithms/dp/MinimumCoins.java renamed to src/main/java/algorithm/dp/MinimumCoins.java

@@ -1,4 +1,4 @@
-package algorithms.dp;
+package algorithm.dp;
 
 import java.util.Collections;
 import java.util.List;