* Numarul posibilitatilor de acoperire a unei table cu dominouri
* Memoizare (trading space for time)

h3. Grafuri
 
* Parcurgeri
** dfs, bfs, meet in the middle bfs
** Componente biconexe
** Componente tare-conexe
** Sortare topologica
** Ciclu eulerian
* Drumuri minime
** A*, iterative deepening
** Dijkstra (cu heapuri, cu set-uri, cu AINT-uri, cu coada ca pe TC - Cosmin stie)
** Dijkstra cu costuri mici ;)
** A* has a lot of intuitive appeal for me. If you compare Dijkstra's vs. A*, Dijkstra's is like a puddle of water flooding outwards on a flat floor, whereas A* is like the same puddle expanding on a bumpy and graded floor toward a drain (the target node) at the lowest point in the floor. Instead of spreading out evenly on all sides, the water seeks the path of least resistance, only trying new paths when something gets in its way. The heuristic function is what provides the 'grade' of the hypothetical floor.
** Floyd-Warshall
** Bellman-Ford
** De obicei mai simplu de implementat si cam aceeasi viteza ca si Dijkstra cu heapuri
** Sistem de inegalitati
** Ciclu de cost mediu minim
* Flux
** Edmonds-Karp
** 'Taietura minima':taietura-minima
** Dinic
** Flux maxim de cost minim
** Flux cu capacitati inferioare
** Circulatii
** Problema postasului chinez
* Arbori
** Diametrul, centrul unui arbore
** Testare daca doi arbori sunt izomorfi
** Cod Prufer
* APM
** Prim
** Kruskal
** Al doilea APM
** APM in graf orientat
** Kirchhoff's matrix tree theorem
* Pentru grafuri bipartite: cuplaj maxim, suport minim, multime independenta maxima
* 'LCA':lca-lowest-common-ancestor, RMQ, Level Ancestor, 'Path Decomposition':heavy-path-decomposition
* Colorari de muchii, graf complet/bipartit/oarecare (teorema lui Vizing)
* Grafuri planare
* Grafuri turneu/ciclu hamiltonian
* Implication graph si 2-SAT
 
 

h3. Siruri de caractere
* Hashuri

* Arbori binari de cautare (treaps, AVL, red-black trees)
* 'Cautari ortogonale. Quad trees, kD-trees':cautari-ortogonale

h3. Grafuri
 
* Parcurgeri
** dfs, bfs, meet in the middle bfs
** Componente biconexe
** Componente tare-conexe
** Sortare topologica
** Ciclu eulerian
* Drumuri minime
** A*, iterative deepening
** Dijkstra (cu heapuri, cu set-uri, cu AINT-uri, cu coada ca pe TC - Cosmin stie)
** Dijkstra cu costuri mici ;)
** A* has a lot of intuitive appeal for me. If you compare Dijkstra's vs. A*, Dijkstra's is like a puddle of water flooding outwards on a flat floor, whereas A* is like the same puddle expanding on a bumpy and graded floor toward a drain (the target node) at the lowest point in the floor. Instead of spreading out evenly on all sides, the water seeks the path of least resistance, only trying new paths when something gets in its way. The heuristic function is what provides the 'grade' of the hypothetical floor.
** Floyd-Warshall
** Bellman-Ford
** De obicei mai simplu de implementat si cam aceeasi viteza ca si Dijkstra cu heapuri
** Sistem de inegalitati
** Ciclu de cost mediu minim
* Flux
** Edmonds-Karp
** 'Taietura minima':taietura-minima
** Dinic
** Flux maxim de cost minim
** Flux cu capacitati inferioare
** Circulatii
** Problema postasului chinez
* Arbori
** Diametrul, centrul unui arbore
** Testare daca doi arbori sunt izomorfi
** Cod Prufer
* APM
** Prim
** Kruskal
** Al doilea APM
** APM in graf orientat
** Kirchhoff's matrix tree theorem
* Pentru grafuri bipartite: cuplaj maxim, suport minim, multime independenta maxima
* 'LCA':lca-lowest-common-ancestor, RMQ, Level Ancestor, 'Path Decomposition':heavy-path-decomposition
* Colorari de muchii, graf complet/bipartit/oarecare (teorema lui Vizing)
* Grafuri planare
* Grafuri turneu/ciclu hamiltonian
* Implication graph si 2-SAT
 

h2. Diverse
h3. 'STL @(Standard Template Library)@':stl