Advanced Algorithms In Addition To Data Structures

As a software engineer, you lot’ll come across countless programming challenges that initially look confusing, hard, or even impossible. Don’t despair! Many of these “new” problems already accept well-established solutions. Advanced Algorithms too Data Structures teaches y'all powerful approaches to a broad reach of tricky coding challenges that you can suit as well as apply to your own applications. Providing a balanced blend of classic, advanced, in addition to new algorithms, this practical guide upgrades your programming toolbox amongst novel perspectives together with hands-on techniques.

Purchase of the print volume includes a complimentary eBook in PDF, Kindle, in addition to ePub formats from Manning Publications.

Can you amend the speed together with efficiency of your applications without investing in new hardware? Well, yes, you lot can: Innovations in algorithms and information structures have led to huge advances in application performance. Pick upwardly this volume to detect a collection of advanced algorithms that will make you a more than effective developer.

Advanced Algorithms together with Data Structures introduces a collection of algorithms for complex programming challenges inward data analysis, motorcar learning, too graph computing. You’ll find cutting-edge approaches to a diversity of tricky scenarios. You’ll even acquire to design your ain data structures for projects that ask a custom solution.

Build on basic data structures you lot already know

Profile your algorithms to speed upward application

Store too inquiry strings efficiently

Distribute clustering algorithms amongst MapReduce

Solve logistics problems using graphs and optimization algorithms

For intermediate programmers.

Marcello La Rocca is a inquiry scientist together with a total-stack engineer. His focus is on optimization algorithms, genetic algorithms, machine learning, and quantum computing.

one Introducing data structures

PART i IMPROVING OVER BASIC DATA STRUCTURES

two Improving priority queues: d-fashion heaps

three Treaps: Using randomization to balance binary search trees

four Bloom filters: Reducing the memory for tracking content

five Disjoint sets: Sub-linear fourth dimension processing

six Trie, radix trie: Efficient string search

vii Use instance: LRU cache

PART two MULTIDEMENSIONAL QUERIES

8 Nearest neighbors search

nine G-d trees: Multidimensional data indexing

ten Similarity Search Trees: Approximate nearest neighbors search for icon retrieval

xi Applications of nearest neighbour search

12 Clustering

thirteen Parallel clustering: MapReduce as well as canopy clustering

PART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER

fourteen An introduction to graphs: Finding paths of minimum distance

xv Graph embeddings as well as planarity: Drawing graphs with minimal border intersections

sixteen Gradient descent: Optimization problems (not merely) on graphs

17 Simulated annealing: Optimization beyond local minima

eighteen Genetic algorithms: Biologically inspired, fast-converging optimization