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      Download the Game Design and Indie Game Marketing Freebook   07/19/17

      GameDev.net and CRC Press have teamed up to bring a free ebook of content curated from top titles published by CRC Press. The freebook, Practices of Game Design & Indie Game Marketing, includes chapters from The Art of Game Design: A Book of Lenses, A Practical Guide to Indie Game Marketing, and An Architectural Approach to Level Design. The GameDev.net FreeBook is relevant to game designers, developers, and those interested in learning more about the challenges in game development. We know game development can be a tough discipline and business, so we picked several chapters from CRC Press titles that we thought would be of interest to you, the GameDev.net audience, in your journey to design, develop, and market your next game. The free ebook is available through CRC Press by clicking here. The Curated Books The Art of Game Design: A Book of Lenses, Second Edition, by Jesse Schell Presents 100+ sets of questions, or different lenses, for viewing a game’s design, encompassing diverse fields such as psychology, architecture, music, film, software engineering, theme park design, mathematics, anthropology, and more. Written by one of the world's top game designers, this book describes the deepest and most fundamental principles of game design, demonstrating how tactics used in board, card, and athletic games also work in video games. It provides practical instruction on creating world-class games that will be played again and again. View it here. A Practical Guide to Indie Game Marketing, by Joel Dreskin Marketing is an essential but too frequently overlooked or minimized component of the release plan for indie games. A Practical Guide to Indie Game Marketing provides you with the tools needed to build visibility and sell your indie games. With special focus on those developers with small budgets and limited staff and resources, this book is packed with tangible recommendations and techniques that you can put to use immediately. As a seasoned professional of the indie game arena, author Joel Dreskin gives you insight into practical, real-world experiences of marketing numerous successful games and also provides stories of the failures. View it here. An Architectural Approach to Level Design This is one of the first books to integrate architectural and spatial design theory with the field of level design. The book presents architectural techniques and theories for level designers to use in their own work. It connects architecture and level design in different ways that address the practical elements of how designers construct space and the experiential elements of how and why humans interact with this space. Throughout the text, readers learn skills for spatial layout, evoking emotion through gamespaces, and creating better levels through architectural theory. View it here. Learn more and download the ebook by clicking here. Did you know? GameDev.net and CRC Press also recently teamed up to bring GDNet+ Members up to a 20% discount on all CRC Press books. Learn more about this and other benefits here.


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  1. Well I am not interested in the solution which is trivial to obtain but in the quality of future solutions. Let me put it this way: There are a total of H locations at which observations can be made.   Then if I have initially N locations in A (N >=4) at a given time then which of location from set H that are not in A will be most optimal for addition to A at N+1. That is when you use condition numbers to test the quality of possible future solutions for addition into A.   So the point is to test the quality of the future solution not using location in A but using the observation in B. By checking which one observation from H can improve the quality of future solution without ever solving the least square itself.    However checking condition number of B would be incorrect as it is not involved in inversion when finding X.  So there must a reason to relate condition number of A and B together for this linear system.
  2. That doesn't seem right. If you can multiply A times X, the number of columns in A has to match the number of rows in X. The transpose can take care of such issues. They are not important but if you wish it can be rewritten as:   A = N x 4 X = 4 x M B = N x M 
  3. I want to test the stability of system using a non-invertible matrix B of the system instead of A, when X is unknown and A, B are both known. I am not interested in the solution directly but want to reduce error in the resulting solution by reducing condition number of B instead of A. All of them are matrices: X = 4 x M,  A = 4 x N, B = N x M.
  4. I have a linear system like: AX = B Normally to measure how stable the solution will be for such a system condition numbers are used. With X being the only unknown the condition number will be checked for A normally. However in this case A only defines the locations at which observations in B are made.   So checking the condition number and reducing it over multiple observations of A does not benefit me much as observed B at those locations might still be contributing to errors significantly in estimated X.   Instead if I check the condition number of B and try to reduce it over multiple observations by selecting A that reduces condition number of B I can more reliably estimate X.   But the issue is to justify the use of evaluating condition number for B which is not involved in inversion in the above linear system. Otherwise it seems incorrect to evaluate the condition number of B instead of A.   Normally condition numbers are defined to be the measure of relative error in X divided by the relative error in B etc.    It shows they are related so there must be a way to justify the use of minimizing condition number of B instead of A in this system and I want to know of some opinion or suggestions for this.