These are my recent Pin​board​.in links:

• [1112.4323] Between the­ory and prac­tice: guide­lines for an opti­miza­tion scheme with genetic algo­rithms — Part I: single-​​objective con­tin­u­ous global optimization

  The rapid advances in the field of opti­miza­tion meth­ods in many pure and applied sci­ence pose the dif­fi­culty of keep­ing track of the devel­op­ments as well as select­ing an appro­pri­ate tech­nique that best suits the prob­lem in-​​hand. From a prac­ti­tioner point of view is right­ful to wan­der “which opti­miza­tion method is the best for my prob­lem?”. Look­ing at the opti­miza­tion process as a “sys­tem” of inter­con– nected parts, in this paper are col­lected some ideas about how to tackle an opti­miza­tion prob­lem using a class of tools from evo­lu­tion­ary com­pu­ta­tions called Genetic Algo­rithms. Despite the num­ber of opti­miza­tion tech­niques avail­able nowa­days the author of this paper thinks that Genetic Algo­rithms still play a cen­tral role for their ver­sa­til­ity, robust­ness, the­o­ret­i­cal frame­work and sim­plic­ity of use. The paper can be con­sid­ered a “col­lec­tion of tips” (from lit­er­a­ture and per­sonal expe­ri­ence) for the non-​​computer-​​scientist that has to deal with opti­miza­tion prob­lems both in the sci­ence and engi­neer­ing prac­tice. No orig­i­nal meth­ods or algo­rithms are proposed.
  
  meta-​​optimization pragmatism-​​almost genetic-​​algorithm agile-​​almost project-​​management

• [1112.6075] A semi­def­i­nite pro­gram­ming approach for solv­ing Mul­ti­ob­jec­tive Lin­ear Programming

  Sev­eral algo­rithms are avail­able in the lit­er­a­ture for find­ing the entire set of Pareto-​​optimal solu­tions in Mul­ti­Ob­jec­tive Lin­ear Pro­gram­ming (MOLP). How­ever, it has not been pro­posed so far an inte­rior point algo­rithm that finds all Pareto-​​optimal solu­tions of MOLP. We present an explicit con­struc­tion, based on a trans­for­ma­tion of any MOLP into a finite sequence of Semi­Def­i­nite Pro­grams (SDP), the solu­tions of which give the entire set of Pareto-​​optimal extreme points solu­tions of MOLP. These SDP prob­lems are solved by inte­rior point meth­ods; thus our approach pro­vides a pseudo-​​polynomial inte­rior point method­ol­ogy to find the set of Pareto-​​optimal solu­tions of MOLP.
  
  linear-​​programming algo­rithms multiobjective-​​optimization nudge-​​targets operations-​​research

• [1112.0826] Clus­ter­ing under Per­tur­ba­tion Resilience

  Recently, Bilu and Linial for­mal­ized an implicit assump­tion often made when choos­ing a clus­ter­ing objec­tive: that the opti­mum clus­ter­ing to the objec­tive should be pre­served under small mul­ti­plica­tive per­tur­ba­tions to dis­tances between points. They showed that for max-​​cut clus­ter­ing it is pos­si­ble to cir­cum­vent NP-​​hardness and obtain polynomial-​​time algo­rithms for instances resilient to large (fac­tor $O(sqrt{n})$) per­tur­ba­tions, and sub­se­quently Awasthi et al. con­sid­ered center-​​based objec­tives, giv­ing algo­rithms for instances resilient to O(1) fac­tor per­tur­ba­tions. In this paper, we greatly advance this line of work. For center-​​based objec­tives, we present an algo­rithm that can opti­mally clus­ter instances resilient to $(1 + sqrt{2})$-factor per­tur­ba­tions, solv­ing an open prob­lem of Awasthi et al. For a com­monly used center-​​based objec­tive $k$-median, we addi­tion­ally give algo­rithms for a more relaxed assump­tion in which we allow the opti­mal solu­tion to change in a small $epsilon$ frac­tion of the points after per­tur­ba­tion. We give the first bounds known for this more real­is­tic and more gen­eral set­ting. We also pro­vide pos­i­tive results for min-​​sum clus­ter­ing which is a gen­er­ally much harder objec­tive than $k$-median (and also non-​​center-​​based). Our algo­rithms are based on new link­age cri­te­ria that may be of inde­pen­dent inter­est. Addi­tion­ally, we give sublinear-​​time algo­rithms, show­ing algo­rithms that can return an implicit clus­ter­ing from only access to a small ran­dom sample.
  
  clus­ter­ing sta­tis­tics nonparametric-​​methods robust­ness resilience algo­rithms nudge-​​targets

• [1104.3516] An adap­tive hier­ar­chi­cal domain decom­po­si­tion method for par­al­lel con­tact dynam­ics sim­u­la­tions of gran­u­lar materials

  A fully par­al­lel ver­sion of the con­tact dynam­ics (CD) method is pre­sented in this paper. For large enough sys­tems, 100% effi­ciency has been demon­strated for up to 256 proces­sors using a hier­ar­chi­cal domain decom­po­si­tion with dynamic load bal­anc­ing. The iter­a­tive scheme to cal­cu­late the con­tact forces is left domain-​​wise sequen­tial, with data exchange after each iter­a­tion step, which ensures its sta­bil­ity. The num­ber of addi­tional iter­a­tions required for con­ver­gence by the par­tially par­al­lel updates at the domain bound­aries becomes neg­li­gi­ble with increas­ing num­ber of par­ti­cles, which allows for an effec­tive par­al­leliza­tion. Com­pared to the sequen­tial imple­men­ta­tion, we found no influ­ence of the par­al­leliza­tion on sim­u­la­tion results.
  
  sim­u­la­tion condensed-​​matter granular-​​materials complex-​​systems