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We study the online decision making problem (ODMP) as a natural generalization of online linear programming. In ODMP, a single decision maker undertakes a sequence of decisions over $T$ time steps. At each time step, the decision maker makes a locally feasible decision based on information available up to that point. The objective is to maximize the accumulated reward while satisfying some convex global constraints called goal constraints. The decision made at each step results in an $m$-dimensional vector that represents the contribution of this local decision to the goal constraints. In the online setting, these goal constraints are soft constraints that can be violated moderately. To handle potential nonconvexity and nonlinearity in ODMP, we propose a Fenchel dual-based online algorithm. At each time step, the algorithm requires solving a potentially nonconvex optimization problem over the local feasible set and a convex optimization problem over the goal set. Under certain stochastic input models, we show that the algorithm achieves $O(\sqrt{mT})$ goal constraint violation deterministically, and $\tilde{O}(\sqrt{mT})$ regret in expected reward. Numerical experiments on an online knapsack problem and an assortment optimization problem are conducted to demonstrate the potential of our proposed online algorithm.