From Wikipedia:In computer science, hill climbing is a mathematical optimization  technique which belongs to the family of local search. It is an  iterative algorithm that starts with an arbitrary solution to a  problem, then attempts to find a better solution by incrementally  changing a single element of the solution. If the change produces a  better solution, an incremental change is made to the new solution,  repeating until no further improvements can be found.I wonder if there are differences between hill climbing method and coordinate descent/ascent method? They seem the same to me.Thanks and regards!
Hill climbing and coordinate descent/ascent
optimization
Hill climbing = optimization. Coordinate descent is ''search in turn along each coordinate'', changing one variable at a time, and is a very specific optimization technique. (Very slow, too, except for separable problems.)But please read more substantial stuff than wikipedia to see how optimization is done and how the terminology is actually used, rather than asking here about everything that wikipedia leaves open. This site is not a language course in optimization!Online references to some excellent books/surveys/papers/websites on optimization:A. Non-heuristic (Usually requiring gradients or subgradients):J. Nocedal and S.J. Wright,Numerical Optimization, 2006.http://home.agh.edu.pl/~pba/pdfdoc/Numerical_Optimization.pdfS. Boyd and L. Vandenberghe, Convex Optimization, 2004.http://www.stanford.edu/~boyd/cvxbookYu. Nesterov, Introductory lectures on convex optimization, 2003.http://www.core.ucl.ac.be/~nesterov/Courses/INMA2460/Intro-nl.pdfA. Neumaier, Complete Search in Continuous Global Optimization and Constraint Satisfaction, 2004http://www.mat.univie.ac.at/~neum/papers.html#glopt03 (this is by me)B. Derivative-free optimization (mostly heuristic)L.M. Rios and N.V. Sahinidis,Derivative-free optimization: A review of algorithms and comparison of software implementations. http://thales.cheme.cmu.edu/dfo/dfo.pdfN. Hansen et al. Comparing Results of 31 Algorithms from the Black-BoxOptimization Benchmarking BBOB-2009.http://www.lri.fr/~hansen/gecco09-results-2010.pdfM. Lozano,  D. Molina,  C. Garca-Martnez,  F. HerreraEvolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems.http://sci2s.ugr.es/eamhco/C: Repository of papers on optimizationA repository of eprints about optimisation and related topics.http://www.optimization-online.org/D: Online access to mostly excellent solversNEOS Solvers - NEOS Server for Optimizationhttp://www.neos-server.org/neos/solvers/index.html