Inspired by the human expert thinking paradigm in operations research, this work introduces a new concept of reasoning tasks: Textual Constrained Optimization (TCO) problems. A TCO problem is characterized by a natural language description that implicitly specifies an underlying structured model with variables, constraints, and objectives. Then, we propose a novel Syllogism-driven Textual Constrained Optimization Reasoning (STCOR) paradigm, which is driven by classical syllogistic logic. Unlike contemporary stepwise methods, our framework structures reasoning into three phases: meta-modeling, which acts as the major premise by retrieving a relevant class-driven prototype template; formalization, which serves as the minor premise by instantiating the template into an explicit logical model from textual queries; and solving, which derives the final answer as conclusion. To support the end to end implementation, we further develop a tri-level optimization algorithm TriRL. This algorithm enables the joint training of all core components to ensure the coherence and efficiency of the entire reasoning system. Extensive experiments on multiple benchmarks, including a TCO benchmark we developed, demonstrate that STCOR achieves significant accuracy improvements (over 10% on average) while enabling the explicit tracing and rectification of reasoning leaks and logical fallacies.