The barrier methods solve the nonlinear problem by solving a sequence of penalized problems. The relation between the sequence, known as external, of the solutions of the penalized functions and the solution of the initial problem was established in the Sixties. In this thesis, we used a logarithmic barrier function. At each external iteration, SQP techniques produce a series of quadratic subproblems whose solutions form a sequence, known as internal, of descent directions, to solve the penalized nonlinear problem. We introduced a change of variable on the step what allow us to obtain optimality conditions more stable numerically. We gave simulations to compare the performances of the G.C. method with that of D.C. method, applied to solve trust-region quadratic problems. We adapted D.C. method to solve the vertical subproblems, which allowed us to reduce their dimensions from n+m to m+p (pTheevolutionofthealgorithmiscontrolledbythemeritfunction.Numericaltestsmakeitpossibletocomparetheadvantagesofvariousformsofthethem.Weintroducednewrulestoimprovethisevolution.Thenumericalexperimentsshowaprofitconcerningthenumberofsolvedproblems.ThestudyoftheconvergenceofourmethodSDC,closesthiswork.Lesmeˊthodesbarrieˋresproposentdereˊsoudreleprobleˋmenonlineˊaireenreˊsolvantunesuitedeprobleˋmespeˊnaliseˊs.Lelienentrelasuite,diteexterne,dessolutionsdesfonctionspeˊnaliseˊesetlasolutionduprobleˋmeinitialaeˊteˊeˊtabliedanslesanneˊessoixante.Danscettetheˋse,nousavonsutiliseˊunefonctionbarrieˋrelogarithmique.Achaqueiteˊrationexterne,latechniqueSQPsechargedeproduireuneseˊriedesous−probleˋmesquadratiquesdontlessolutionsformentunesuite,diteinterne,dedirectionsdedescentepourreˊsoudreleprobleˋmenonlineˊairepeˊnaliseˊ.Nousavonsintroduitunchangementdevariablesurlepasdedeˊplacementcequiapermisd′obtenirdesconditionsd′optimaliteˊplusstablenumeˊriquement.NousavonsreˊaliseˊdessimulationsnumeˊriquespourcomparerlesperformancesdelameˊthodedesgradientsconjugueˊsaˋcelledelameˊthodeD.C.,appliqueˊespourreˊsoudredesprobleˋmesquadratiquesdereˊgiondeconfiance.NousavonsadapteˊlameˊthodeD.C.pourreˊsoudrelessous−probleˋmesverticaux,cequinousapermisderamenerleursdimensionsden+maˋm+p( p L'évolution de l'algorithme est contrôlée par la fonction de mérite. Des tests numériques permettent de comparer les avantages de différentes formes de la fonction de mérite. Nous avons introduit de nouvelles règles pour améliorer cette évolution. Les expériences numériques montrent un gain concernant le nombre de problèmes résolus. L'étude de la convergence de notre méthode SDC, clôt ce travail